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      <text xml:space="preserve" bytes="588">&lt;strong&gt;Brain Development &amp; Education Lab Wiki&lt;/strong&gt;

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== Getting started ==
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  <page>
    <title>Anatomy Pipeline</title>
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      <comment>Created page with &quot;We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the proc...&quot;</comment>
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      <text xml:space="preserve" bytes="821">We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
=AC-PC Alignment=
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [[mrAnatAverageAcpcNifti https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m]]</text>
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      <text xml:space="preserve" bytes="822">We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
=AC-PC Alignment=
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [[ https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]]</text>
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      <text xml:space="preserve" bytes="819">We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
=AC-PC Alignment=
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]</text>
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      <text xml:space="preserve" bytes="1018">We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
=AC-PC Alignment=
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.

&lt;syntaxhighlight lang=&quot;python&quot;&gt;
test
&lt;/syntaxhighlight&gt;</text>
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      <text xml:space="preserve" bytes="987">We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
=AC-PC Alignment=
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
=Freesurfer Segmentation=</text>
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      <text xml:space="preserve" bytes="1000">We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Alignment==
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
==Freesurfer Segmentation==

__TOC__</text>
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      <text xml:space="preserve" bytes="1000">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Alignment==
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
==Freesurfer Segmentation==</text>
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      <text xml:space="preserve" bytes="1037">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Alignment==
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.

 parrec2nii
 mrAnatAverageAcpcNifti
==Freesurfer Segmentation==</text>
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      <text xml:space="preserve" bytes="1036">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Alignment==
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.

 parrec2nii
mrAnatAverageAcpcNifti
==Freesurfer Segmentation==</text>
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      <comment>/* AC-PC Alignment */</comment>
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      <text xml:space="preserve" bytes="1038">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Alignment==
Data can come off the scanner with arbitrary header information and the subject might not be properly position. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.

 parrec2nii
 mrAnatAverageAcpcNifti

==Freesurfer Segmentation==</text>
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      </contributor>
      <comment>/* AC-PC Alignment */</comment>
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      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1025">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.

 parrec2nii -c
 mrAnatAverageAcpcNifti

==Freesurfer Segmentation==</text>
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      <comment>/* AC-PC Aligned Nifti Image */</comment>
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      <text xml:space="preserve" bytes="1179">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
 cd /home/projects/MRI/[subid]
 parrec2nii -c --scaling=fp *.PAR
 im = niftiRead('T1path')
 mrAnatAverageAcpcNifti({'T1path'}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', diag(im.qto_xyz))

==Freesurfer Segmentation==</text>
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      <timestamp>2015-08-17T21:03:18Z</timestamp>
      <contributor>
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        <id>1</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
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      <text xml:space="preserve" bytes="1741">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
 cd /home/projects/MRI/[subid]
 parrec2nii -c --scaling=fp *.PAR
 im = niftiRead('T1path')
 mrAnatAverageAcpcNifti({'T1path'}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', diag(im.qto_xyz))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')</text>
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      <text xml:space="preserve" bytes="2379">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
 cd /home/projects/MRI/[subid]
 parrec2nii -c --scaling=fp *.PAR
 im = niftiRead('T1path')
 mrAnatAverageAcpcNifti({'T1path'}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', diag(im.qto_xyz))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
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      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2382">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
 im = niftiRead('T1path')
 mrAnatAverageAcpcNifti({'T1path'}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', diag(im.qto_xyz))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>s5uwbheou5aamlm0qblewj5lk89lsno</sha1>
    </revision>
    <revision>
      <id>37</id>
      <parentid>36</parentid>
      <timestamp>2015-09-03T18:57:17Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2408">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
 T1path = mri_rms('T1path')
 im = niftiRead(T1path)
 mrAnatAverageAcpcNifti({'T1path'}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', diag(im.qto_xyz))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>jfxsu23c21hqh1qmt2lil6f46e49ek4</sha1>
    </revision>
    <revision>
      <id>38</id>
      <parentid>37</parentid>
      <timestamp>2015-09-03T19:02:14Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2412">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
 T1path = mri_rms('T1path')
 im = niftiRead(T1path)
 mrAnatAverageAcpcNifti({'T1path'}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], diag(im.qto_xyz))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>rdqjayl7ecd2g5icm8dfd5ie2fe5v63</sha1>
    </revision>
    <revision>
      <id>39</id>
      <parentid>38</parentid>
      <timestamp>2015-09-03T19:14:57Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2720">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>cp0yippc3d6a9eghh6d4g37t48q37hw</sha1>
    </revision>
    <revision>
      <id>41</id>
      <parentid>39</parentid>
      <timestamp>2015-09-03T19:59:01Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2763">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

[[File:Ac-pc.jpg|200px|thumb|left|ac-pc]]

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>3khhoslv7ff0wmifac7mj4c14qlpj0s</sha1>
    </revision>
    <revision>
      <id>42</id>
      <parentid>41</parentid>
      <timestamp>2015-09-03T19:59:24Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2765">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

[[File:Ac-pc.jpg|200px|thumb|center|ac-pc]]

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>lcfiscdrc6dypgkqvhfbjzpxvitaphj</sha1>
    </revision>
    <revision>
      <id>43</id>
      <parentid>42</parentid>
      <timestamp>2015-09-03T23:16:15Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2759">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

[[File:Ac-pc.jpg|400px|thumb|center]]

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>idll1gckounruclm2hh8ywmozeidfgj</sha1>
    </revision>
    <revision>
      <id>44</id>
      <parentid>43</parentid>
      <timestamp>2015-09-04T18:08:30Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2758">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))
 [[File:Ac-pc.jpg|300px|thumb|right]]

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>p41gwh719o1j2b7jpd7uakbffno57wx</sha1>
    </revision>
    <revision>
      <id>45</id>
      <parentid>44</parentid>
      <timestamp>2015-09-04T18:08:55Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2757">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)) [[File:Ac-pc.jpg|300px|thumb|right]]

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>3k7o7xozz2ka7di1yioq0ph3eh9q1r6</sha1>
    </revision>
    <revision>
      <id>46</id>
      <parentid>45</parentid>
      <timestamp>2015-09-04T18:20:13Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2757">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image [[File:Ac-pc.jpg|300px|thumb|right]]
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>kg2nmckaz20z9o5sqo1jsr4tavcudgu</sha1>
    </revision>
    <revision>
      <id>47</id>
      <parentid>46</parentid>
      <timestamp>2015-09-04T18:23:37Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2764">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory. [[File:Ac-pc.jpg|300px|thumb|right|bottom]]
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>dpqc1t3zbduno47yutqffxtjoz3mro2</sha1>
    </revision>
    <revision>
      <id>48</id>
      <parentid>47</parentid>
      <timestamp>2015-09-04T18:24:27Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2769">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory. [[File:Ac-pc.jpg|300px|thumb|right|text-bottom]]
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>fmbenyq8vbowg6xoh7a024pu9ymz32g</sha1>
    </revision>
    <revision>
      <id>49</id>
      <parentid>48</parentid>
      <timestamp>2015-09-04T18:25:51Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2763">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory. [[File:Ac-pc.jpg|300px|right|text-bottom]]
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>cayonb9jvm9jh5az1crktuzm3m0u0cm</sha1>
    </revision>
    <revision>
      <id>50</id>
      <parentid>49</parentid>
      <timestamp>2015-09-04T18:27:45Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2751">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')

==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>k4ucri693zaug9alvp4f8rjn4728mho</sha1>
    </revision>
    <revision>
      <id>51</id>
      <parentid>50</parentid>
      <timestamp>2015-09-30T23:22:19Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2113">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz); % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')</text>
      <sha1>kv2g8d41fbfljm7ehyj2db3nmkerecf</sha1>
    </revision>
    <revision>
      <id>53</id>
      <parentid>51</parentid>
      <timestamp>2015-10-01T19:09:12Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2114">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b --scaling=fp *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')</text>
      <sha1>mmmukn9h4hnb3ujv06bm21qrtjm3m2w</sha1>
    </revision>
    <revision>
      <id>107</id>
      <parentid>53</parentid>
      <timestamp>2015-11-02T21:10:24Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2101">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc_.nii.gz')</text>
      <sha1>q2mbvhrs6vm2zk6nrcfvby15nj9d384</sha1>
    </revision>
    <revision>
      <id>108</id>
      <parentid>107</parentid>
      <timestamp>2015-11-02T21:20:19Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2100">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>3d78migyjvneadelkunhghw5tjn0d6g</sha1>
    </revision>
    <revision>
      <id>109</id>
      <parentid>108</parentid>
      <timestamp>2015-11-02T21:24:05Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2314">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>osmw2gveyt7sgmwx0unktybh4uu8y34</sha1>
    </revision>
    <revision>
      <id>123</id>
      <parentid>109</parentid>
      <timestamp>2015-11-03T01:02:32Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2323">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>nqbiz2khgggogbwfuxelm48smlrfyxs</sha1>
    </revision>
    <revision>
      <id>129</id>
      <parentid>123</parentid>
      <timestamp>2015-11-06T19:43:38Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2332">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>csos9wqvtla1s2tgfa1nqyra0mnzgbq</sha1>
    </revision>
    <revision>
      <id>130</id>
      <parentid>129</parentid>
      <timestamp>2015-11-06T19:44:27Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2341">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>germq6s145e51agwrev4949lpd1rxdc</sha1>
    </revision>
    <revision>
      <id>131</id>
      <parentid>130</parentid>
      <timestamp>2015-11-06T19:44:58Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2350">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>64hofr0bzmxqbh5v75gp19qn2058o4y</sha1>
    </revision>
    <revision>
      <id>132</id>
      <parentid>131</parentid>
      <timestamp>2015-11-06T19:48:32Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2588">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>fjurycp4kxkk4wn5ycw4kno4ivg8ktj</sha1>
    </revision>
    <revision>
      <id>177</id>
      <parentid>132</parentid>
      <timestamp>2015-11-20T20:30:05Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2589">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>4tth0gbe4h4ilnre0yxo08qa6st49ct</sha1>
    </revision>
    <revision>
      <id>183</id>
      <parentid>177</parentid>
      <timestamp>2015-12-02T02:05:33Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2588">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>6yz6veto85mh7aq5ffn09i86giz7jhy</sha1>
    </revision>
    <revision>
      <id>200</id>
      <parentid>183</parentid>
      <timestamp>2015-12-03T22:14:38Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* AC-PC Aligned Nifti Image */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2592">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.&lt;br&gt;
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>jspe2u9se4yw47qstnfh7z1ssxx3iot</sha1>
    </revision>
    <revision>
      <id>201</id>
      <parentid>200</parentid>
      <timestamp>2015-12-03T22:19:55Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2905">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==Data Organization/Naming Convention==
Step 1: Create a subject ID folder within the anatomy directory
 NLR_###_FL_MR#
For Longitudinal scans, you will want to create a folder for each individual scan, as well as a general directory NLR_###_FL to store an AC-PC aligned image of the total average across scans. 
==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.&lt;br&gt;
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>8axsrka9wqilfz8utylrqm6eowjoqfg</sha1>
    </revision>
    <revision>
      <id>202</id>
      <parentid>201</parentid>
      <timestamp>2015-12-03T22:20:38Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Data Organization/Naming Convention */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2905">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==Data Organization/Naming Convention==
Step 1: Create a subject ID folder within the anatomy directory
 NLR_###_FL_MR#
For Longitudinal scans, you will want to create a folder for each individual scan, as well as a general directory NLR_###_FL to store an AC-PC aligned image of the total average across scans.

==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.&lt;br&gt;
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>aixf3tgjp2byubqmm605ol7x21cn0f2</sha1>
    </revision>
    <revision>
      <id>203</id>
      <parentid>202</parentid>
      <timestamp>2015-12-03T22:25:43Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Data Organization/Naming Convention */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2909">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==Data Organization/Naming Convention==
Step 1: Create a subject ID folder within the anatomy directory
 NLR_###_FL_MR#
For Longitudinal scans, you will want to create a folder for each individual scan, as well as a general directory NLR_###_FL to store an AC-PC aligned image of the total average across scans.&lt;br&gt;

==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.&lt;br&gt;
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
      <sha1>3ufyq7kbr96orni1afgkq78san6717h</sha1>
    </revision>
    <revision>
      <id>204</id>
      <parentid>203</parentid>
      <timestamp>2015-12-03T22:27:38Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Data Organization/Naming Convention */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2981">__TOC__

We collect a high resolution T1-weighted image on every subject, and use this image to define the coordinate space for all subsequent analyses. This section describes the processing steps for a subject's T1-weighted anatomy and should be performed before analyzing the rest of their MRI data.
==Data Organization/Naming Convention==
Step 1: Create a subject ID folder within the anatomy directory
 NLR_###_FL_MR#
For Longitudinal scans, you will want to create a folder for each individual scan, as well as a general directory NLR_###_FL to store an AC-PC aligned image of the total average across scans.&lt;br&gt;
 [subid] --seen below-- will include the _MR# for each individual scan.

==AC-PC Aligned Nifti Image==
[[File:Ac-pc.jpg|300px|right]]
Data can come off the scanner with arbitrary header information and in parrec format. So for each subject we start by defining a coordinate frame where 0,0,0 is at the anterior commissure, the anterior and posterior commissure are in the same X and Z planes, and the mid-line is centered in the image. Bob Dougherty wrote a nice tool to help with this. See [https://github.com/vistalab/vistasoft/blob/master/mrAnatomy/VolumeUtilities/mrAnatAverageAcpcNifti.m mrAnatAverageAcpcNifti]. The subject's T1-weighted image should be ac-pc aligned, resliced (preserving its resolution), and saved in the subject's anatomy directory.&lt;br&gt;
Step 1: In a terminal, convert the PAR/REC files to nifti images
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3)')

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/mnt/diskArray/projects/anatomy/[subid]/t1_acpc.nii.gz')
For subjects with multiple scans, the output destination should be [subid]_MR[#].  For example, a subject's (205_AB) second scan for the NLR study would be written: NLR_205_AB_MR2.  This is in line with the Freesurfer system of notation.</text>
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    </revision>
  </page>
  <page>
    <title>Behavioral</title>
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        <username>Pdonnelly</username>
        <id>2</id>
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      <comment>Created page with &quot;=Reading Battery=&quot;</comment>
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      <text xml:space="preserve" bytes="424">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)</text>
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      <text xml:space="preserve" bytes="1200">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV
 The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 *Letter-Word Identification:
  Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English.</text>
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We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV
 The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
 As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
  &lt;ul type=&quot;circle&quot;&gt;
  &lt;ul&gt;Letter-Word Identification:
  Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English.</text>
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We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;ul&gt;Letter-Word Identification:
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English.</text>
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We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;Letter-Word Identification:
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English.</text>
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      <text xml:space="preserve" bytes="2550">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;Letter-Word Identification:
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;Word Attack: Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;Oral Reading: Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;Sentence Reading Fluency: Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;</text>
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      <text xml:space="preserve" bytes="2555">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;Letter-Word Identification:
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;Word Attack: Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;Oral Reading: Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;Sentence Reading Fluency: Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;</text>
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      <text xml:space="preserve" bytes="2579">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;</text>
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      <id>94</id>
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      <text xml:space="preserve" bytes="3034">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;br&gt;
&lt;br&gt;
TOWRE-2 &lt;br&gt;
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;</text>
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      <text xml:space="preserve" bytes="3026">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;


TOWRE-2 &lt;br&gt;
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;</text>
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      <text xml:space="preserve" bytes="3031">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

TOWRE-2 &lt;br&gt;
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;</text>
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      <text xml:space="preserve" bytes="6170">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

WJ-IV &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

TOWRE-2 &lt;br&gt;
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

WASI &lt;br&gt;
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

CTOPP-2 &lt;br&gt;
The CTOPP-2 is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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      <text xml:space="preserve" bytes="6178">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

=WJ-IV= &lt;br&gt;
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

=TOWRE-2= &lt;br&gt;
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

=WASI= &lt;br&gt;
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

=CTOPP-2= &lt;br&gt;
The CTOPP-2 is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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    <revision>
      <id>99</id>
      <parentid>98</parentid>
      <timestamp>2015-10-28T22:37:39Z</timestamp>
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      <text xml:space="preserve" bytes="6162">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

=WJ-IV= 
The WJ-IV Tests of Achievement contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

=TOWRE-2= 
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

=WASI= 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

=CTOPP-2= 
The CTOPP-2 is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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      <text xml:space="preserve" bytes="6251">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

=WJ-IV= 
The [WJ-IV Tests of Achievement] contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

=TOWRE-2= 
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

=WASI= 
The WASI is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

=CTOPP-2= 
The CTOPP-2 is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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      <text xml:space="preserve" bytes="6324">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

=WJ-IV= 
The &lt;a href=&quot;http://www.riversidepublishing.com/products/wj-iv/index.html&quot;&gt;WJ-IV Tests of Achievement&lt;/a&gt; contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

=TOWRE-2= 
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

=WASI= 
The WASI is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

=CTOPP-2= 
The CTOPP-2 is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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      <text xml:space="preserve" bytes="6312">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

=WJ-IV= 
The [http://www.riversidepublishing.com/products/wj-iv/index.html WJ-IV Tests of Achievement] contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

=TOWRE-2= 
The TOWRE-2 contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

=WASI= 
The WASI is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

=CTOPP-2= 
The CTOPP-2 is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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      <text xml:space="preserve" bytes="6579">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

=WJ-IV= 
The [http://www.riversidepublishing.com/products/wj-iv/index.html WJ-IV Tests of Achievement] contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

=TOWRE-2= 
The [http://www.proedinc.com/customer/productview.aspx?id=5074 TOWRE-2] contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

=WASI= 
The [http://www.pearsonclinical.com/psychology/products/100000037/wechsler-abbreviated-scale-of-intelligence--second-edition-wasi-ii.html#tab-details WASI] is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

=CTOPP-2= 
The [http://www.proedinc.com/customer/productview.aspx?id=5187 CTOPP-2] is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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    <revision>
      <id>104</id>
      <parentid>103</parentid>
      <timestamp>2015-10-28T22:51:53Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
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      <text xml:space="preserve" bytes="6586">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

==WJ-IV==
The [http://www.riversidepublishing.com/products/wj-iv/index.html WJ-IV Tests of Achievement] contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

==TOWRE-2== 
The [http://www.proedinc.com/customer/productview.aspx?id=5074 TOWRE-2] contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

==WASI== 
The [http://www.pearsonclinical.com/psychology/products/100000037/wechsler-abbreviated-scale-of-intelligence--second-edition-wasi-ii.html#tab-details WASI] is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

==CTOPP-2== 
The [http://www.proedinc.com/customer/productview.aspx?id=5187 CTOPP-2] is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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    <revision>
      <id>105</id>
      <parentid>104</parentid>
      <timestamp>2015-10-28T23:01:15Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
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      <comment>/* WJ-IV */</comment>
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      <text xml:space="preserve" bytes="6721">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

==WJ-IV==
The [http://www.riversidepublishing.com/products/wj-iv/index.html WJ-IV Tests of Achievement] contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

 Administration: &lt;br&gt;
 Materials needed: &lt;br&gt;
 # Examiner Test Record
 # Response Booklet
 # Test Booklet
 # Audio Recorder
 # Pencil

==TOWRE-2== 
The [http://www.proedinc.com/customer/productview.aspx?id=5074 TOWRE-2] contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

==WASI== 
The [http://www.pearsonclinical.com/psychology/products/100000037/wechsler-abbreviated-scale-of-intelligence--second-edition-wasi-ii.html#tab-details WASI] is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

==CTOPP-2== 
The [http://www.proedinc.com/customer/productview.aspx?id=5187 CTOPP-2] is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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      <id>106</id>
      <parentid>105</parentid>
      <timestamp>2015-10-28T23:01:51Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
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      <text xml:space="preserve" bytes="6586">=Reading Battery=

We use a variety of standardized measures of reading aptitude as a part of our work in the BDE Lab. Below is a list of the measures we utilize, a description of their use, and details of their administration.
 Woodcock-Johnson IV Tests of Achievement (WJ-IV)
 Test of Word Reading Efficiency-2 (TOWRE-2)
 Weschler Abbreviated Scale of Intelligence (WASI)
 Comprehensive Test of Phonological Processing-2 (CTOPP-2)

==WJ-IV==
The [http://www.riversidepublishing.com/products/wj-iv/index.html WJ-IV Tests of Achievement] contains 20 tests measuring reading, mathematics, written language, and academic knowledge.  
As a part of the study, only four tests were used, all focusing on basic reading skills and fluency.  
 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Letter-Word Identification''':
Letter-Word Identification measures the examinee’s word identification skills, a reading-writing (Grw) ability.  The initial items require the individual to identify letters that appear in large type on the examinee’s side of the Test Book.  The remaining items require the person to read aloud individual words correctly. The examinee is not required to know       the meaning of any word.  The items become increasingly difficult as the selected words appear less frequently in written English. &lt;/li&gt;
&lt;li&gt;'''Word Attack''': Word Attack measures a person’s ability to apply phonic and structural analysis skills to the pronunciation of unfamiliar printed words, a reading-writing (Grw) ability. The initial items require the individual to produce the sounds for single letters.  The remaining items require the person to read aloud letter combinations that are phonically consistent or are regular patterns in English orthography but are nonsense or low-frequency words.  The items become more difficult as the complexity of the nonsense words increases.&lt;/li&gt;
&lt;li&gt;'''Oral Reading''': Oral Reading is a measure of story reading accuracy and prosody, a reading-writing (Grw) ability.  The individual reads aloud sentences that gradually increase in difficulty.  Performance is scored for both accuracy and fluency of expression.&lt;/li&gt;
&lt;li&gt;'''Sentence Reading Fluency''': Sentence Reading Fluency measures reading rate, requiring both reading-writing (Grw) and cognitive processing speed (Gs) abilities.  The task involves reading simple sentences silently and quickly in the Response Booklet, deciding if the statement is true or false, and then circling Yes or No.  The difficulty level of the sentences gradually increases to a moderate level.  The individual attempts to complete as many items as possible within a 3-minute time limit.&lt;/li&gt;
&lt;/ul&gt;

==TOWRE-2== 
The [http://www.proedinc.com/customer/productview.aspx?id=5074 TOWRE-2] contains two subsets, each of which has four alternate forms. 
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''The Sight Word Efficiency''' subtest assesses the number of real words printed in vertical lists that an individual can accurately identify within 45 seconds.&lt;/li&gt;
&lt;li&gt;'''Phonemic Decoding Efficiency''' subtest measures the number of pronounceable nonwords presented in vertical lists than an individual can accurately decode within 45 seconds.&lt;/li&gt;
&lt;/ul&gt;

==WASI== 
The [http://www.pearsonclinical.com/psychology/products/100000037/wechsler-abbreviated-scale-of-intelligence--second-edition-wasi-ii.html#tab-details WASI] is a shortened test of general cognitive ability, both verbal and non-verbal.
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Vocabulary''': The vocabulary subtest has 31 items, including 3 picture items and 28 verbal items.  For picture items, the examinee names the object presented visually.  For verbal items, the examinee defines words that are presented visually and orally.  Vocabulary is designed to measure an examinee’s word knowledge and verbal concept formation.  &lt;/li&gt;
&lt;li&gt;'''Matrix Reasoning''': The Matrix Reasoning subtest has 30 items.  The examinee views a series of incomplete matrices and completes each one by selecting the correct response option.  The subtest taps … classification and spatial ability, knowledge of part-whole relationships, simultaneous processing, and perceptual organization. &lt;/li&gt;
&lt;/ul&gt;

==CTOPP-2== 
The [http://www.proedinc.com/customer/productview.aspx?id=5187 CTOPP-2] is multi-part test that measures phonological awareness, phonological memory, and rapid naming skills.  We utilized the following tests:
&lt;ul type=&quot;circle&quot;&gt;
&lt;li&gt;'''Elision''': This 34-item subtest measures the extent to which an individual can say a word and then say what is left after dropping out designated sounds.  For the first two items, the examiner says compound words and asks the examinee to say that word and then say the word that remains after dropping one of the compound words.  For the remaining items, the individual listens to a word and repeats that word and then is asked to say the word without a specific sound.  For example, the examinee is instructed, “Say bold.” After repeating “bold,” the examinee is told, “Now say ‘bold’ without saying /b/.” The correct response is “old.”&lt;/li&gt;
&lt;li&gt;'''Memory for Digits''': This 28-item subtest measures the extent to which an individual can repeat a series of numbers ranging in length from two to eight digits.  After the individual has listened to a series of audio-recorded numbers presented at a rate of 2 per second, he or she is asked to repeat the numbers in the same order in which they were heard.&lt;/li&gt;
&lt;li&gt;'''Rapid Digit Naming''': This 36-item subtest measures the speed with which an individual can name numbers.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged numbers (i.e., 2, 3, 4, 5, 7, 8). The examinee is instructed to name the numbers on the top row from left to right, and then name the numbers on the next row from left to right, and so on, until all of the numbers have been named.  The individual’s score is the total number of seconds taken to name all of the numbers on the page.&lt;/li&gt;
&lt;li&gt;'''Rapid Letter Naming''': This 36-item subtest measures the speed with which an individual can name letters.  The Picture Book contains one page for this subtest, which consists of four rows and nine columns of six randomly arranged letters (i.e., a, c, k, n, s, t). The examinee is instructed to name the letters on the top row from left to right, and then name the letters on the next row from left to right, and so on, until all of the letters have been named.  The individual’s score is the total number of seconds taken to name all of the letters.&lt;/li&gt;
&lt;/ul&gt;</text>
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    <title>Brain Development &amp; Education Lab</title>
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      <text xml:space="preserve" bytes="1489">__TOC__

Page describing how we analyze cortical thickness with freesurfer

Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
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      <parentid>114</parentid>
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      <contributor>
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      <text xml:space="preserve" bytes="1506">__TOC__

Page describing how we analyze cortical thickness with freesurfer

Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image and then ac-pc align. If a subject has multiple images then the rms operation should be run on each image and then a cell-array with paths to all the images can be pushed through mrAnatAverageAcpcNifti resulting in a very nice anatomy
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image
 im = niftiRead(T1path); % Read root mean squared image
 voxres = diag(im.qto_xyz)'; % Get the voxel resolution of the image (mm)
 mrAnatAverageAcpcNifti({T1path}, '/home/projects/anatomy/[subid]/t1_acpc.nii.gz', [], voxres(1:3))

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
Or even better use this handy matlab function written by Jon Winawer to run freesurfer and then also build some useful files that we like to use for data visualization such as a high resolution gray/white segmentation.
 fs_autosegmentToITK([subid], '/home/projects/anatomy/[subid]/t1_acpc.nii.gz')</text>
      <sha1>7g65w75f5skh0ivxszm6pdxmjqfb764</sha1>
    </revision>
    <revision>
      <id>116</id>
      <parentid>115</parentid>
      <timestamp>2015-11-02T21:33:37Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="920">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all</text>
      <sha1>g0dybwfiahn713tyfeu4ozk5p0oqhmo</sha1>
    </revision>
    <revision>
      <id>117</id>
      <parentid>116</parentid>
      <timestamp>2015-11-02T21:35:15Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1088">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [[https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial|Freesurfer Wiki]] for analyzing cortical thickness</text>
      <sha1>il1snkj26sqikso3nbwjvcme6sw5vpa</sha1>
    </revision>
    <revision>
      <id>118</id>
      <parentid>117</parentid>
      <timestamp>2015-11-02T21:37:05Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1086">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial Freesurfer Wiki] for analyzing cortical thickness</text>
      <sha1>isonm5r5l4m0vvlgw1ug0i8g9rwgdwh</sha1>
    </revision>
    <revision>
      <id>119</id>
      <parentid>118</parentid>
      <timestamp>2015-11-02T21:45:13Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1536">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Setting up Freesurfer and MATLAB==
First make sure that you have the correct lines in your .bashrc file to run freesurfer:
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/diskArray/projects/freesurfer
Next make sure that you have the right tool boxes in your matlab search path. This should be done through your startup.m file
 addpath(genpath('~/git/yeatmanlab'));


==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial Freesurfer Wiki] for analyzing cortical thickness</text>
      <sha1>nswo96a1909rkzb0gkzaees2t1ma79a</sha1>
    </revision>
    <revision>
      <id>120</id>
      <parentid>119</parentid>
      <timestamp>2015-11-02T21:45:36Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Setting up Freesurfer and MATLAB */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1535">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Setting up Freesurfer and MATLAB==
First make sure that you have the correct lines in your .bashrc file to run freesurfer:
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/diskArray/projects/freesurfer
Next make sure that you have the right tool boxes in your matlab search path. This should be done through your startup.m file
 addpath(genpath('~/git/yeatmanlab'));

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial Freesurfer Wiki] for analyzing cortical thickness</text>
      <sha1>f7ceao7le41xd5un6d9h16zm1t5oscd</sha1>
    </revision>
    <revision>
      <id>121</id>
      <parentid>120</parentid>
      <timestamp>2015-11-03T01:00:40Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1638">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Setting up Freesurfer and MATLAB==
First make sure that you have the correct lines in your .bashrc file to run freesurfer:
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/diskArray/projects/freesurfer
Next make sure that you have the right tool boxes in your matlab search path. This should be done through your startup.m file
 addpath(genpath('~/git/yeatmanlab'));

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /home/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial Freesurfer Wiki] for analyzing cortical thickness. The first steps are described [https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing here]</text>
      <sha1>6c1hyp7rjm89x6jqfnb43113glrlky7</sha1>
    </revision>
    <revision>
      <id>122</id>
      <parentid>121</parentid>
      <timestamp>2015-11-03T01:01:38Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Create a T1 weighted nifti image for the subject */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1647">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Setting up Freesurfer and MATLAB==
First make sure that you have the correct lines in your .bashrc file to run freesurfer:
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/diskArray/projects/freesurfer
Next make sure that you have the right tool boxes in your matlab search path. This should be done through your startup.m file
 addpath(genpath('~/git/yeatmanlab'));

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/anatomy/[subid]/t1_acpc_.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial Freesurfer Wiki] for analyzing cortical thickness. The first steps are described [https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing here]</text>
      <sha1>e9puscn7sw3c30vxodvabf6gdqwgcwk</sha1>
    </revision>
    <revision>
      <id>189</id>
      <parentid>122</parentid>
      <timestamp>2015-12-03T21:15:47Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Freesurfer Segmentation */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1683">__TOC__

Page describing how we analyze cortical thickness with freesurfer

==Setting up Freesurfer and MATLAB==
First make sure that you have the correct lines in your .bashrc file to run freesurfer:
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/diskArray/projects/freesurfer
Next make sure that you have the right tool boxes in your matlab search path. This should be done through your startup.m file
 addpath(genpath('~/git/yeatmanlab'));

==Create a T1 weighted nifti image for the subject==
Step 1: In a terminal, convert the PAR/REC files to nifti images. You may not need to do this if you have already gone through the [[Anatomy_Pipeline|Anatomy Pipeline]]
 cd /mnt/diskArray/projects/MRI/[subid]
 parrec2nii -c -b *.PAR
Step 2: In MATLAB compute the root mean squared (RMS) image. Once again this might have already been done in the [[Anatomy_Pipeline|Anatomy Pipeline]] so you can re-use that RMS image
 T1path = 'Path to t1 weighted image';
 T1path = mri_rms(T1path); % Root mean squared image

==Freesurfer Segmentation==
Freesurfer is a useful tool for segmenting a T1-weighted image and building a cortical mesh. To segment the subject's T1-weighted image using freesurfer from the command line type:
 recon-all -i /home/projects/MRI/[subjid]/[YYYYMMDD]/[subjid]_WIP_MEMP_VBM_SENSE_13_1_MSE.nii.gz -subjid [subid] -all
We will follow the steps outlined in the [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial Freesurfer Wiki] for analyzing cortical thickness. The first steps are described [https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing here]</text>
      <sha1>ryg73alq3sv9y51l0cyanfy8iyhofda</sha1>
    </revision>
  </page>
  <page>
    <title>Data Analysis</title>
    <ns>0</ns>
    <id>2</id>
    <revision>
      <id>3</id>
      <timestamp>2015-08-13T18:52:01Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;This page documents our data analysis&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="37">This page documents our data analysis</text>
      <sha1>q4myjr48qfypj7glg22pvjt829z3fjt</sha1>
    </revision>
  </page>
  <page>
    <title>Data Organization</title>
    <ns>0</ns>
    <id>34</id>
    <revision>
      <id>191</id>
      <timestamp>2015-12-03T21:42:33Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>Created page with &quot;MRI:  NLR_###_FL			—Parent 	YYYYMMDD		—Scan Date 		*.PAR		—Raw data 		*.REC 		*.nii			—Converted nifty images 		*MSE.nii.gz	—Root mean squared image  Anatomy:  NLR_#...&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="573">MRI:

NLR_###_FL			—Parent
	YYYYMMDD		—Scan Date
		*.PAR		—Raw data
		*.REC
		*.nii			—Converted nifty images
		*MSE.nii.gz	—Root mean squared image

Anatomy:

NLR_###_FL		—AC-PC aligned, longitudinal average
	t1_acpc.nii.gz
NLR_###_FL_MR#	—AC-PC aligned, individual scan
	t1_acpc.nii.gz

Freesurfer:

NLR_###_FL_MR#							—Individual Segmentation
NLR_###_FL_MR#.long.NLR_###_FL_MRbase	—Longitudinal base comp for ea scan
NLR_###_FL_MRbase						—Base average segmentation
qdec									—Longitudinal data storage
	long.qdec.table.dat						—data table</text>
      <sha1>4rup2ap8pukvex5efbc9oku4bwjv2i1</sha1>
    </revision>
    <revision>
      <id>192</id>
      <parentid>191</parentid>
      <timestamp>2015-12-03T21:48:48Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="894">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
NLR_###_FL			—Parent&lt;br&gt;
	YYYYMMDD		—Scan Date&lt;br&gt;
		*.PAR		—Raw data&lt;br&gt;
		*.REC&lt;br&gt;
		*.nii			—Converted nifty images&lt;br&gt;
		*MSE.nii.gz	—Root mean squared image&lt;br&gt;

Anatomy:

NLR_###_FL		—AC-PC aligned, longitudinal average
	t1_acpc.nii.gz
NLR_###_FL_MR#	—AC-PC aligned, individual scan
	t1_acpc.nii.gz

Freesurfer:

NLR_###_FL_MR#							—Individual Segmentation
NLR_###_FL_MR#.long.NLR_###_FL_MRbase	—Longitudinal base comp for ea scan
NLR_###_FL_MRbase						—Base average segmentation
qdec									—Longitudinal data storage
	long.qdec.table.dat						—data table</text>
      <sha1>4y72kj1p0s7vnnhqqun1gib687z693q</sha1>
    </revision>
    <revision>
      <id>193</id>
      <parentid>192</parentid>
      <timestamp>2015-12-03T21:50:12Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* MRI */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="899">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
NLR_###_FL			—Parent&lt;br&gt;
:YYYYMMDD:::—Scan Date&lt;br&gt;
::*.PAR:::—Raw data&lt;br&gt;
::*.REC&lt;br&gt;
::*.nii	:::—Converted nifty images&lt;br&gt;
::*MSE.nii.gz:::—Root mean squared image&lt;br&gt;

Anatomy:

NLR_###_FL		—AC-PC aligned, longitudinal average
	t1_acpc.nii.gz
NLR_###_FL_MR#	—AC-PC aligned, individual scan
	t1_acpc.nii.gz

Freesurfer:

NLR_###_FL_MR#							—Individual Segmentation
NLR_###_FL_MR#.long.NLR_###_FL_MRbase	—Longitudinal base comp for ea scan
NLR_###_FL_MRbase						—Base average segmentation
qdec									—Longitudinal data storage
	long.qdec.table.dat						—data table</text>
      <sha1>q61i60woie1fwtu8ptdpm638aeml448</sha1>
    </revision>
    <revision>
      <id>194</id>
      <parentid>193</parentid>
      <timestamp>2015-12-03T21:51:46Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* MRI */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="909">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
NLR_###_FL			—Parent&lt;br&gt;
:YYYYMMDD{{pad|4.0em}}—Scan Date&lt;br&gt;
::*.PAR:::—Raw data&lt;br&gt;
::*.REC&lt;br&gt;
::*.nii	:::—Converted nifty images&lt;br&gt;
::*MSE.nii.gz:::—Root mean squared image&lt;br&gt;

Anatomy:

NLR_###_FL		—AC-PC aligned, longitudinal average
	t1_acpc.nii.gz
NLR_###_FL_MR#	—AC-PC aligned, individual scan
	t1_acpc.nii.gz

Freesurfer:

NLR_###_FL_MR#							—Individual Segmentation
NLR_###_FL_MR#.long.NLR_###_FL_MRbase	—Longitudinal base comp for ea scan
NLR_###_FL_MRbase						—Base average segmentation
qdec									—Longitudinal data storage
	long.qdec.table.dat						—data table</text>
      <sha1>dtceke2uec23qh0cpru0u2kexqf7vfn</sha1>
    </revision>
    <revision>
      <id>195</id>
      <parentid>194</parentid>
      <timestamp>2015-12-03T21:55:15Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* MRI */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1015">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
NLR_###_FL			—Parent&lt;br&gt;
:YYYYMMDD—Scan Date&lt;br&gt;
::*.PAR—Raw data&lt;br&gt;
::*.REC&lt;br&gt;
::*.nii	—Converted nifty images&lt;br&gt;
::*MSE.nii.gz—Root mean squared image&lt;br&gt;

==Anatomy==
Within /mnt/diskArray/projects/anatomy:&lt;br&gt;
NLR_###_FL—AC-PC aligned, longitudinal average&lt;br&gt;
:t1_acpc.nii.gz&lt;br&gt;
NLR_###_FL_MR#—AC-PC aligned, individual scan&lt;br&gt;
:t1_acpc.nii.gz&lt;br&gt;

==Freesurfer==
Within /mnt/diskArray/projects/freesurfer:&lt;br&gt;
NLR_###_FL_MR#							—Individual Segmentation&lt;br&gt;
NLR_###_FL_MR#.long.NLR_###_FL_MRbase	—Longitudinal base comp for ea scan&lt;br&gt;
NLR_###_FL_MRbase						—Base average segmentation&lt;br&gt;
qdec									—Longitudinal data storage&lt;br&gt;
:long.qdec.table.dat						—data table&lt;br&gt;</text>
      <sha1>lv6pcsy51nudy3og3xc39vvnukyc4a4</sha1>
    </revision>
    <revision>
      <id>196</id>
      <parentid>195</parentid>
      <timestamp>2015-12-03T21:56:16Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* MRI */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1051">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
'''NLR_###_FL'''			—Parent&lt;br&gt;
:'''YYYYMMDD'''—Scan Date&lt;br&gt;
::'''*.PAR'''—Raw data&lt;br&gt;
::'''*.REC'''&lt;br&gt;
::'''*.nii	'''—Converted nifty images&lt;br&gt;
::'''*MSE.nii.gz'''—Root mean squared image&lt;br&gt;

==Anatomy==
Within /mnt/diskArray/projects/anatomy:&lt;br&gt;
NLR_###_FL—AC-PC aligned, longitudinal average&lt;br&gt;
:t1_acpc.nii.gz&lt;br&gt;
NLR_###_FL_MR#—AC-PC aligned, individual scan&lt;br&gt;
:t1_acpc.nii.gz&lt;br&gt;

==Freesurfer==
Within /mnt/diskArray/projects/freesurfer:&lt;br&gt;
NLR_###_FL_MR#							—Individual Segmentation&lt;br&gt;
NLR_###_FL_MR#.long.NLR_###_FL_MRbase	—Longitudinal base comp for ea scan&lt;br&gt;
NLR_###_FL_MRbase						—Base average segmentation&lt;br&gt;
qdec									—Longitudinal data storage&lt;br&gt;
:long.qdec.table.dat						—data table&lt;br&gt;</text>
      <sha1>i02t645vsohm03s7gk2ytrpdgoespi8</sha1>
    </revision>
    <revision>
      <id>197</id>
      <parentid>196</parentid>
      <timestamp>2015-12-03T21:59:49Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1105">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
'''NLR_###_FL'''			—Parent&lt;br&gt;
:'''YYYYMMDD'''—Scan Date&lt;br&gt;
::'''*.PAR'''—Raw data&lt;br&gt;
::'''*.REC'''&lt;br&gt;
::'''*.nii	'''—Converted nifty images&lt;br&gt;
::'''*MSE.nii.gz'''—Root mean squared image&lt;br&gt;

==Anatomy==
Within /mnt/diskArray/projects/anatomy:&lt;br&gt;
'''NLR_###_FL'''—AC-PC aligned, longitudinal average&lt;br&gt;
:'''t1_acpc.nii.gz'''&lt;br&gt;
'''NLR_###_FL_MR#'''—AC-PC aligned, individual scan&lt;br&gt;
:'''t1_acpc.nii.gz'''&lt;br&gt;

==Freesurfer==
Within /mnt/diskArray/projects/freesurfer:&lt;br&gt;
'''NLR_###_FL_MR#'''							—Individual Segmentation&lt;br&gt;
'''NLR_###_FL_MR#.long.NLR_###_FL_MRbase'''	—Longitudinal base comp for ea scan&lt;br&gt;
'''NLR_###_FL_MRbase'''						—Base average segmentation&lt;br&gt;
'''qdec'''									—Longitudinal data storage&lt;br&gt;
:'''long.qdec.table.dat'''						—data table&lt;br&gt;</text>
      <sha1>qy7ft63c9c5ov0om6lnkf5zvd9r1jv8</sha1>
    </revision>
    <revision>
      <id>198</id>
      <parentid>197</parentid>
      <timestamp>2015-12-03T22:00:42Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Anatomy */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1105">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
'''NLR_###_FL'''			—Parent&lt;br&gt;
:'''YYYYMMDD'''—Scan Date&lt;br&gt;
::'''*.PAR'''—Raw data&lt;br&gt;
::'''*.REC'''&lt;br&gt;
::'''*.nii	'''—Converted nifty images&lt;br&gt;
::'''*MSE.nii.gz'''—Root mean squared image&lt;br&gt;

==Anatomy==
Within /mnt/diskArray/projects/anatomy:&lt;br&gt;
'''NLR_###_FL'''&lt;br&gt;
:'''t1_acpc.nii.gz'''—AC-PC aligned, longitudinal average&lt;br&gt;
'''NLR_###_FL_MR#'''&lt;br&gt;
:'''t1_acpc.nii.gz'''—AC-PC aligned, individual scan&lt;br&gt;

==Freesurfer==
Within /mnt/diskArray/projects/freesurfer:&lt;br&gt;
'''NLR_###_FL_MR#'''							—Individual Segmentation&lt;br&gt;
'''NLR_###_FL_MR#.long.NLR_###_FL_MRbase'''	—Longitudinal base comp for ea scan&lt;br&gt;
'''NLR_###_FL_MRbase'''						—Base average segmentation&lt;br&gt;
'''qdec'''									—Longitudinal data storage&lt;br&gt;
:'''long.qdec.table.dat'''						—data table&lt;br&gt;</text>
      <sha1>asp6vqdb9hpywwe41yy6cbqyd6myuf5</sha1>
    </revision>
    <revision>
      <id>199</id>
      <parentid>198</parentid>
      <timestamp>2015-12-03T22:01:17Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1110">In the server, the nomenclature for file naming is as follows.  This will allow for ease in adapting to the naming structures within Freesurfer, but also will provide a stable system by which we can keep all of the longitudinal data organized and clear.

==MRI==
Within /mnt/diskArray/projects/MRI:&lt;br&gt;
'''NLR_###_FL'''			—Parent&lt;br&gt;
:'''YYYYMMDD'''—Scan Date&lt;br&gt;
::'''*.PAR'''—Raw data&lt;br&gt;
::'''*.REC'''&lt;br&gt;
::'''*.nii	'''—Converted nifty images&lt;br&gt;
::'''*MSE.nii.gz'''—Root mean squared image&lt;br&gt;

==Anatomy==
Within /mnt/diskArray/projects/anatomy:&lt;br&gt;
'''NLR_###_FL'''&lt;br&gt;
:'''t1_acpc.nii.gz'''—AC-PC aligned, longitudinal average&lt;br&gt;
'''NLR_###_FL_MR#'''&lt;br&gt;
:'''t1_acpc.nii.gz'''—AC-PC aligned, individual scan&lt;br&gt;

==Freesurfer==
Within /mnt/diskArray/projects/freesurfer:&lt;br&gt;
:'''NLR_###_FL_MR#'''							—Individual Segmentation&lt;br&gt;
:'''NLR_###_FL_MR#.long.NLR_###_FL_MRbase'''	—Longitudinal base comp for ea scan&lt;br&gt;
:'''NLR_###_FL_MRbase'''						—Base average segmentation&lt;br&gt;
:'''qdec'''									—Longitudinal data storage&lt;br&gt;
::'''long.qdec.table.dat'''						—data table&lt;br&gt;</text>
      <sha1>2zhrsoonbm7c3w1jhhdzblq5pg0gtx1</sha1>
    </revision>
  </page>
  <page>
    <title>Diffusion Pipeline</title>
    <ns>0</ns>
    <id>7</id>
    <revision>
      <id>33</id>
      <timestamp>2015-09-01T19:05:24Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;Describe our tools for processing dMRI data&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="43">Describe our tools for processing dMRI data</text>
      <sha1>tl1g1is1jm3gk2f983fbvmltzwnfzg8</sha1>
    </revision>
    <revision>
      <id>52</id>
      <parentid>33</parentid>
      <timestamp>2015-09-30T23:22:36Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="636">==Preprocess diffusion data==
If diffusion data was acquired on the subject we want to (a) correct for EPI distortions in the data using FSL's topup tool; (b) correct for subject motion and eddy currents; (c) fit a tensor model and create a dt6.mat file; (d) fit the CSD model with mrtrix; (e) run AFQ to segment the fibers into all the major fiber groups. Jason Yeatman has written a helpful utility to run FSL's topup and eddy functions:
 fsl_preprocess(dwi_files, bvecs_file, bvals_file, pe_dir, outdir)
This function is also wrapped within another utility to run this whole pipeline (steps a-e) on a subject
 bde_preprocessdiffusion</text>
      <sha1>mcyhwsu9khtwl6pvw8w19cr51f7kjsd</sha1>
    </revision>
  </page>
  <page>
    <title>FMRI</title>
    <ns>0</ns>
    <id>37</id>
    <revision>
      <id>225</id>
      <timestamp>2016-01-14T18:26:54Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;=Organize the subject's data= Put raw data into:  subjid/date/raw Hereafter referred to as the session directory Make event files (parfiles) and put the in  subjid/date/Stimul...&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="295">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit</text>
      <sha1>cs05lafxm0nwjvw7z8scmtd3rm7he9p</sha1>
    </revision>
    <revision>
      <id>226</id>
      <parentid>225</parentid>
      <timestamp>2016-01-14T18:30:43Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="409">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit

=Open Inplane view=
 mrVista
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan</text>
      <sha1>894v9ns9pzr0hk1kcl65lo02uzljzxb</sha1>
    </revision>
    <revision>
      <id>230</id>
      <parentid>226</parentid>
      <timestamp>2016-01-14T18:32:59Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="448">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit
[[File:mrInit.png|200px|left|alt text]]
=Open Inplane view=
 mrVista
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan</text>
      <sha1>0cqkaxbutv8w63s0wgjv87a2jc2p42a</sha1>
    </revision>
    <revision>
      <id>231</id>
      <parentid>230</parentid>
      <timestamp>2016-01-14T18:33:27Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="448">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit
[[File:mrInit.png|400px|center|mrInit]]
=Open Inplane view=
 mrVista
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan</text>
      <sha1>i3m88oo3h7sqivcxjoxzf7u3cptc0c0</sha1>
    </revision>
    <revision>
      <id>232</id>
      <parentid>231</parentid>
      <timestamp>2016-01-14T18:35:09Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Initialize an mrVista session */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="495">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit
[[File:mrInit.png|400px|center|mrInit]]

[[File:mrInit_SessionDesc.png|400px|center]]

=Open Inplane view=
 mrVista
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan</text>
      <sha1>fwwr0zlr6szw6zra0490twh7zhsqtbg</sha1>
    </revision>
    <revision>
      <id>233</id>
      <parentid>232</parentid>
      <timestamp>2016-01-14T18:35:44Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Initialize an mrVista session */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="555">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit
Add files to mrVista session
[[File:mrInit.png|400px|center|mrInit]]
Fill in the session description
[[File:mrInit_SessionDesc.png|400px|center]]

=Open Inplane view=
 mrVista
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan</text>
      <sha1>2hj6av0nlgvzdoltblprm7nnsiqnsdm</sha1>
    </revision>
    <revision>
      <id>234</id>
      <parentid>233</parentid>
      <timestamp>2016-01-14T18:36:58Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Open Inplane view */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="593">=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit
Add files to mrVista session
[[File:mrInit.png|400px|center|mrInit]]
Fill in the session description
[[File:mrInit_SessionDesc.png|400px|center]]

=Open Inplane view=
 mrVista
[[File:Inplaneview.png|400px|center]]
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan</text>
      <sha1>9ylgc14dkyfrlt59oor47t399qkn25d</sha1>
    </revision>
    <revision>
      <id>235</id>
      <parentid>234</parentid>
      <timestamp>2016-01-14T18:41:13Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="979">__TOC__

=Organize the subject's data=
Put raw data into:
 subjid/date/raw
Hereafter referred to as the session directory
Make event files (parfiles) and put the in
 subjid/date/Stimuli/parfiles

=Initialize an mrVista session=
In a MATLAB terminal change to the session directory and open mrInit
 mrInit
Add files to mrVista session
[[File:mrInit.png|400px|center|mrInit]]
Fill in the session description
[[File:mrInit_SessionDesc.png|400px|center]]

=Open Inplane view=
 mrVista
[[File:Inplaneview.png|400px|center]]
And run motion correction
 Analysis -&gt; Motion Compensation -&gt; Within + Between Scan

=Fit GLM=
First associate a parfile with each scan and then group all the MotionComp scans so that the GLM is fit to all of them
 GLM -&gt; Assign Parfiles to Scan
 GLM -&gt; Grouping -&gt; Group Scans
 GLM -&gt; Apply CLM/Contrast New Code
In the GLM window that comes up set the HRF to SPM Difference of gammas and set the number of TRs (in our case 8) and detrend option to Quadratic.</text>
      <sha1>7m3n60sis1ffn0mwv98h9hpdjglwk8p</sha1>
    </revision>
  </page>
  <page>
    <title>FMRI Data Acquisition</title>
    <ns>0</ns>
    <id>36</id>
    <revision>
      <id>217</id>
      <timestamp>2016-01-08T21:11:34Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>Created page with &quot;=Logistics=  ==Scheduling==  Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down  ==Materials Needed==  1. USB  2. Stimulus computer, charger  3. Dongle  4....&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1924">=Logistics=

==Scheduling==
 Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down

==Materials Needed==
 1. USB
 2. Stimulus computer, charger
 3. Dongle
 4. Ensure that WiFi and notifications are turned off on the device being used
 5. Subject Information; including MRI Screening Form(s), MR Safe Glasses (if applicable)

==Set-Up==
 1. Plug in the &quot;Trigger Output&quot; USB cord to the Stimulus computer
 2. Plug in the Video VGA Trigger cord to the stimulus computer using the dongle
 3. Press red &quot;trigger switch&quot; button to switch to the rear green LED light indication
 4. On the output port console within the glass cabinet on the bottom shelf, press 0, 1, 0, 2, 0, 2, 0, 2 to switch communication between the desktop to the laptop and teh projector in the scanner room
 5. Switch button press box to work with numbers as opposed to colors

==Running the Stimulus==
 1. open MatLab
 2. Navigate to runexperimentLocalizer.m
 3. run the command: runexperimentLocalizer(#, subjid_#)
 4. you will run the sequence 3 times for each subject. NOTE the scanner itself will trigger the sequence

==Script==
Before the first fMRI scan runs, say the following to the subject:
 Alright, [subject name], now it's time to play the game that we practiced.  Remember the first thing you're going to see is a gray screen with a white dot in the middle for a little while.  After about 16 seconds the game will start and you're gonna see groups of images.  They'll either be images of words, objects, or faces.  Your job is to be as still as you can and press the button whenever the image repeats right in a row.  Are you ready? ... [wait for subject response] ... Alright! Remember from now until you can't hear the machine running anymore you need to be as still as you possibly can.  Try your best to wait until the end  to scratch an itch and swallow.  Got it? ... [wait for subject response] ... Awesome.  Here we go!</text>
      <sha1>plm7cqo39kzqx292mlvmdok52w20jkg</sha1>
    </revision>
    <revision>
      <id>218</id>
      <parentid>217</parentid>
      <timestamp>2016-01-08T21:12:47Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Logistics */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1942">=Logistics=

==Scheduling==
 Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down

==Materials Needed==
 1. USB
 2. Stimulus computer, charger
 3. Dongle
 4. Ensure that WiFi and notifications are turned off on the device being used
 5. Subject Information; including MRI Screening Form(s), MR Safe Glasses (if applicable)

==Set-Up==
 1. Plug in the &quot;Trigger Output&quot; USB cord to the Stimulus computer
 2. Plug in the Video VGA Trigger cord to the stimulus computer using the dongle
 3. Press red &quot;trigger switch&quot; button to switch to the rear green LED light indication
 4. On the output port console within the glass cabinet on the bottom shelf, press 0, 1, 0, 2, 0, 2, 0, 2 to switch communication between the desktop to the laptop and teh projector in the scanner room
 5. Switch button press box to work with numbers as opposed to colors

==Running the Stimulus==
 1. open MatLab
 2. Navigate to runexperimentLocalizer.m
 3. run the command: runexperimentLocalizer(#, subjid_#)
 4. you will run the sequence 3 times for each subject. NOTE the scanner itself will trigger the sequence

==Script==
Before the first fMRI scan runs, say the following to the subject:
 Alright, [subject name], now it's time to play the game that we practiced.  
 Remember the first thing you're going to see is a gray screen with a white dot in the middle for a little while.  
 After about 16 seconds the game will start and you're gonna see groups of images.  
 They'll either be images of words, objects, or faces.  
 Your job is to be as still as you can and press the button whenever the image repeats right in a row.  
 Are you ready? ... [wait for subject response] ... Alright! 
 Remember from now until you can't hear the machine running anymore you need to be as still as you possibly can.  
 Try your best to wait until the end  to scratch an itch and swallow.  
 Got it? ... [wait for subject response] ... Awesome.  
 Here we go!</text>
      <sha1>71hx13p2bhnfryyqws0ahqwodioxgni</sha1>
    </revision>
    <revision>
      <id>219</id>
      <parentid>218</parentid>
      <timestamp>2016-01-08T21:14:03Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Logistics */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1955">=Logistics=

==Scheduling==
 Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down

==Materials Needed==
 1. USB
 2. Stimulus computer, charger
 3. Dongle
 4. Ensure that WiFi and notifications are turned off on the device being used
 5. Subject Information; including MRI Screening Form(s), MR Safe Glasses (if applicable)

==Set-Up==
 1. Plug in the &quot;Trigger Output&quot; USB cord to the Stimulus computer
 2. Plug in the Video VGA Trigger cord to the stimulus computer using the dongle
 3. Press red &quot;trigger switch&quot; button to switch to the rear green LED light indication
 4. On the output port console within the glass cabinet on the bottom shelf, press 0, 1, 0, 2, 0, 2, 0, 2 to switch communication between the desktop to the laptop and teh projector in the scanner room
 5. Switch button press box to work with numbers as opposed to colors

=Running the Stimulus=

==Procedure==
 1. open MatLab
 2. Navigate to runexperimentLocalizer.m
 3. run the command: runexperimentLocalizer(#, subjid_#)
 4. you will run the sequence 3 times for each subject. NOTE the scanner itself will trigger the sequence

==Script==
Before the first fMRI scan runs, say the following to the subject:
 Alright, [subject name], now it's time to play the game that we practiced.  
 Remember the first thing you're going to see is a gray screen with a white dot in the middle for a little while.  
 After about 16 seconds the game will start and you're gonna see groups of images.  
 They'll either be images of words, objects, or faces.  
 Your job is to be as still as you can and press the button whenever the image repeats right in a row.  
 Are you ready? ... [wait for subject response] ... Alright! 
 Remember from now until you can't hear the machine running anymore you need to be as still as you possibly can.  
 Try your best to wait until the end  to scratch an itch and swallow.  
 Got it? ... [wait for subject response] ... Awesome.  
 Here we go!</text>
      <sha1>79mtqm5fxi55rdg45mda3ju0qmopevk</sha1>
    </revision>
    <revision>
      <id>220</id>
      <parentid>219</parentid>
      <timestamp>2016-01-08T21:17:43Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Running the Stimulus */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2364">=Logistics=

==Scheduling==
 Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down

==Materials Needed==
 1. USB
 2. Stimulus computer, charger
 3. Dongle
 4. Ensure that WiFi and notifications are turned off on the device being used
 5. Subject Information; including MRI Screening Form(s), MR Safe Glasses (if applicable)

==Set-Up==
 1. Plug in the &quot;Trigger Output&quot; USB cord to the Stimulus computer
 2. Plug in the Video VGA Trigger cord to the stimulus computer using the dongle
 3. Press red &quot;trigger switch&quot; button to switch to the rear green LED light indication
 4. On the output port console within the glass cabinet on the bottom shelf, press 0, 1, 0, 2, 0, 2, 0, 2 to switch communication between the desktop to the laptop and teh projector in the scanner room
 5. Switch button press box to work with numbers as opposed to colors

=Running the Stimulus=

==Procedure==
 1. open MatLab
 2. Navigate to runexperimentLocalizer.m
 3. run the command: runexperimentLocalizer(#, subjid_#)
 4. you will run the sequence 3 times for each subject. NOTE the scanner itself will trigger the sequence

==Script==
Before the first fMRI scan runs, say the following to the subject:
 Alright, [subject name], now it's time to play the game that we practiced.  
 Remember the first thing you're going to see is a gray screen with a white dot in the middle for a little while.  
 After about 16 seconds the game will start and you're gonna see groups of images.  
 They'll either be images of words, objects, or faces.  
 Your job is to be as still as you can and press the button whenever the image repeats right in a row.  
 Are you ready? ... [wait for subject response] ... Alright! 
 Remember from now until you can't hear the machine running anymore you need to be as still as you possibly can.  
 Try your best to wait until the end  to scratch an itch and swallow.  
 Got it? ... [wait for subject response] ... Awesome.  
 Here we go!
In Between each run, say the following:
 How was that one, [subject name]?
 Are you ready for the (next/last) run? ... [wait for subject response] ...
 Great! Remember to keep as still as you possibly can.  Here we go.  
If the subject moves during a scan, depending on the amount of movement either add extra stress before the next run, or stop the scan and re-run 
after reminding him/her to be very still.</text>
      <sha1>rin1aewukxe8blg3fkqgvvgjt2ogint</sha1>
    </revision>
    <revision>
      <id>221</id>
      <parentid>220</parentid>
      <timestamp>2016-01-08T21:18:11Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Set-Up */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2366">=Logistics=

==Scheduling==
 Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down

==Materials Needed==
 1. USB
 2. Stimulus computer, charger
 3. Dongle
 4. Ensure that WiFi and notifications are turned off on the device being used
 5. Subject Information; including MRI Screening Form(s), MR Safe Glasses (if applicable)

==Set-Up==
 1. Plug in the &quot;Trigger Output&quot; USB cord to the Stimulus computer
 2. Plug in the Video VGA Trigger cord to the stimulus computer using the dongle
 3. Press red &quot;trigger switch&quot; button to switch to the rear green LED light indication
 4. On the output port console within the glass cabinet on the bottom shelf, press 0, 1, 0, 2, 0, 2, 0, 2 
 to switch communication between the desktop to the laptop and teh projector in the scanner room
 5. Switch button press box to work with numbers as opposed to colors

=Running the Stimulus=

==Procedure==
 1. open MatLab
 2. Navigate to runexperimentLocalizer.m
 3. run the command: runexperimentLocalizer(#, subjid_#)
 4. you will run the sequence 3 times for each subject. NOTE the scanner itself will trigger the sequence

==Script==
Before the first fMRI scan runs, say the following to the subject:
 Alright, [subject name], now it's time to play the game that we practiced.  
 Remember the first thing you're going to see is a gray screen with a white dot in the middle for a little while.  
 After about 16 seconds the game will start and you're gonna see groups of images.  
 They'll either be images of words, objects, or faces.  
 Your job is to be as still as you can and press the button whenever the image repeats right in a row.  
 Are you ready? ... [wait for subject response] ... Alright! 
 Remember from now until you can't hear the machine running anymore you need to be as still as you possibly can.  
 Try your best to wait until the end  to scratch an itch and swallow.  
 Got it? ... [wait for subject response] ... Awesome.  
 Here we go!
In Between each run, say the following:
 How was that one, [subject name]?
 Are you ready for the (next/last) run? ... [wait for subject response] ...
 Great! Remember to keep as still as you possibly can.  Here we go.  
If the subject moves during a scan, depending on the amount of movement either add extra stress before the next run, or stop the scan and re-run 
after reminding him/her to be very still.</text>
      <sha1>loyecjmmlrtwnxtbfzxrwsd4keja3u6</sha1>
    </revision>
    <revision>
      <id>222</id>
      <parentid>221</parentid>
      <timestamp>2016-01-12T00:51:29Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Script */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2473">=Logistics=

==Scheduling==
 Be sure to schedule on the DISC calendar using the NLR_fMRI drop-down

==Materials Needed==
 1. USB
 2. Stimulus computer, charger
 3. Dongle
 4. Ensure that WiFi and notifications are turned off on the device being used
 5. Subject Information; including MRI Screening Form(s), MR Safe Glasses (if applicable)

==Set-Up==
 1. Plug in the &quot;Trigger Output&quot; USB cord to the Stimulus computer
 2. Plug in the Video VGA Trigger cord to the stimulus computer using the dongle
 3. Press red &quot;trigger switch&quot; button to switch to the rear green LED light indication
 4. On the output port console within the glass cabinet on the bottom shelf, press 0, 1, 0, 2, 0, 2, 0, 2 
 to switch communication between the desktop to the laptop and teh projector in the scanner room
 5. Switch button press box to work with numbers as opposed to colors

=Running the Stimulus=

==Procedure==
 1. open MatLab
 2. Navigate to runexperimentLocalizer.m
 3. run the command: runexperimentLocalizer(#, subjid_#)
 4. you will run the sequence 3 times for each subject. NOTE the scanner itself will trigger the sequence

==Script==
Before the first fMRI scan runs, say the following to the subject:
 Alright, [subject name], now it's time to play the game that we practiced.  
 Really quick, do me a favor and without moving your head or looking down, can you push the button for me?
 Remember the first thing you're going to see is a gray screen with a white dot in the middle for a little while.  
 After about 16 seconds the game will start and you're gonna see groups of images.  
 They'll either be images of words, objects, or faces.  
 Your job is to be as still as you can and press the button whenever the image repeats right in a row.  
 Are you ready? ... [wait for subject response] ... Alright! 
 Remember from now until you can't hear the machine running anymore you need to be as still as you possibly can.  
 Try your best to wait until the end  to scratch an itch and swallow.  
 Got it? ... [wait for subject response] ... Awesome.  
 Here we go!
In Between each run, say the following:
 How was that one, [subject name]?
 Are you ready for the (next/last) run? ... [wait for subject response] ...
 Great! Remember to keep as still as you possibly can.  Here we go.  
If the subject moves during a scan, depending on the amount of movement either add extra stress before the next run, or stop the scan and re-run 
after reminding him/her to be very still.</text>
      <sha1>3uklvbo0hxd4raehngiblxoaef0oftw</sha1>
    </revision>
  </page>
  <page>
    <title>Friends &amp; Affiliates</title>
    <ns>0</ns>
    <id>42</id>
    <revision>
      <id>245</id>
      <timestamp>2016-02-18T23:58:20Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>Created page with &quot;=Institute for Learning &amp; Brain Sciences=  *[http://ilabs.washington.edu I-LABS website] *[http://megwiki.ilabs.uw.edu/ MEG Center]&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="131">=Institute for Learning &amp; Brain Sciences=

*[http://ilabs.washington.edu I-LABS website]
*[http://megwiki.ilabs.uw.edu/ MEG Center]</text>
      <sha1>c87y7va08kuvckqboigs7w5nov7bhrz</sha1>
    </revision>
    <revision>
      <id>246</id>
      <parentid>245</parentid>
      <timestamp>2016-02-19T00:07:05Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Institute for Learning &amp; Brain Sciences */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="569">=University of Washington=

*[http://ilabs.washington.edu Institute for Learning &amp; Brain Sciences]
*[http://megwiki.ilabs.uw.edu/ MEG Center]
*[http://depts.washington.edu/labsn/404.php Laboratory for Auditory Brain Sciences and Neuroengineering]
**[https://sites.google.com/a/uw.edu/labsn/ [LABS]^n Wiki]
*[http://depts.washington.edu/ccdl/ Cognition &amp; Cortical Dynamics Laboratory]
*[http://depts.washington.edu/sphsc/ Department of Speech &amp; Hearing Sciences]
*[http://www.psych.uw.edu/ Department of Psychology]
*[http://escience.washington.edu/ E-Science Institute]</text>
      <sha1>rtnvwnz0ys69wtumuheispsbo2i521g</sha1>
    </revision>
    <revision>
      <id>247</id>
      <parentid>246</parentid>
      <timestamp>2016-02-19T00:07:49Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="571">=University of Washington=

*[http://ilabs.washington.edu Institute for Learning &amp; Brain Sciences]
*[http://megwiki.ilabs.uw.edu/ MEG Center]
*[http://depts.washington.edu/labsn/404.php Laboratory for Auditory Brain Sciences and Neuroengineering]
**[https://sites.google.com/a/uw.edu/labsn/ '[LABS]^n' Wiki]
*[http://depts.washington.edu/ccdl/ Cognition &amp; Cortical Dynamics Laboratory]
*[http://depts.washington.edu/sphsc/ Department of Speech &amp; Hearing Sciences]
*[http://www.psych.uw.edu/ Department of Psychology]
*[http://escience.washington.edu/ E-Science Institute]</text>
      <sha1>j9wts4iqmsxt8qpmn71pxz799eps0ps</sha1>
    </revision>
    <revision>
      <id>248</id>
      <parentid>247</parentid>
      <timestamp>2016-02-19T00:08:39Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="579">=University of Washington=

*[http://ilabs.washington.edu Institute for Learning &amp; Brain Sciences]
*[http://megwiki.ilabs.uw.edu/ MEG Center]
*[http://depts.washington.edu/labsn/404.php Laboratory for Auditory Brain Sciences and Neuroengineering]
**[https://sites.google.com/a/uw.edu/labsn/ [LABS]&lt;sup&gt;n&lt;/sup&gt; Wiki]
*[http://depts.washington.edu/ccdl/ Cognition &amp; Cortical Dynamics Laboratory]
*[http://depts.washington.edu/sphsc/ Department of Speech &amp; Hearing Sciences]
*[http://www.psych.uw.edu/ Department of Psychology]
*[http://escience.washington.edu/ E-Science Institute]</text>
      <sha1>qyansfsbstppm1n20cei1iejjmk02pw</sha1>
    </revision>
    <revision>
      <id>249</id>
      <parentid>248</parentid>
      <timestamp>2016-02-19T00:09:31Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="579">=University of Washington=

*[http://ilabs.washington.edu Institute for Learning &amp; Brain Sciences]
*[http://megwiki.ilabs.uw.edu/ MEG Center]
*[http://depts.washington.edu/labsn/404.php Laboratory for Auditory Brain Sciences and Neuroengineering]
**[https://sites.google.com/a/uw.edu/labsn/ (LABS)&lt;sup&gt;n&lt;/sup&gt; Wiki]
*[http://depts.washington.edu/ccdl/ Cognition &amp; Cortical Dynamics Laboratory]
*[http://depts.washington.edu/sphsc/ Department of Speech &amp; Hearing Sciences]
*[http://www.psych.uw.edu/ Department of Psychology]
*[http://escience.washington.edu/ E-Science Institute]</text>
      <sha1>6gtzsmacmhftzvac4rk35qeqwua6mvp</sha1>
    </revision>
    <revision>
      <id>250</id>
      <parentid>249</parentid>
      <timestamp>2016-02-19T00:09:50Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="579">=University of Washington=

*[http://ilabs.washington.edu Institute for Learning &amp; Brain Sciences]
*[http://megwiki.ilabs.uw.edu/ MEG Center]
*[http://depts.washington.edu/labsn/404.php Laboratory for Auditory Brain Sciences and Neuroengineering]
**[https://sites.google.com/a/uw.edu/labsn/ (LABS)&lt;sup&gt;N&lt;/sup&gt; Wiki]
*[http://depts.washington.edu/ccdl/ Cognition &amp; Cortical Dynamics Laboratory]
*[http://depts.washington.edu/sphsc/ Department of Speech &amp; Hearing Sciences]
*[http://www.psych.uw.edu/ Department of Psychology]
*[http://escience.washington.edu/ E-Science Institute]</text>
      <sha1>or8hmmh2gpe7vqj0ndxikj6ilssr6an</sha1>
    </revision>
  </page>
  <page>
    <title>Friends Affiliates</title>
    <ns>0</ns>
    <id>41</id>
    <revision>
      <id>238</id>
      <timestamp>2016-02-18T23:52:09Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>Created page with &quot;=Institute for Learning &amp; Brain Sciences=  [http://ilabs.washington.edu I-LABS Website]&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="87">=Institute for Learning &amp; Brain Sciences=

[http://ilabs.washington.edu I-LABS Website]</text>
      <sha1>mg9ogyfqzqz411xd7xjp3jufdt8idmf</sha1>
    </revision>
    <revision>
      <id>239</id>
      <parentid>238</parentid>
      <timestamp>2016-02-18T23:53:23Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Institute for Learning &amp; Brain Sciences */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="129">=Institute for Learning &amp; Brain Sciences=

[http://ilabs.washington.edu I-LABS website]
[http://megwiki.ilabs.uw.edu/ MEG Center]</text>
      <sha1>t1daqm1ar83gch7excwtjxyy71yp5oz</sha1>
    </revision>
    <revision>
      <id>240</id>
      <parentid>239</parentid>
      <timestamp>2016-02-18T23:53:53Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="134">=Institute for Learning &amp; Brain Sciences=

[http://ilabs.washington.edu I-LABS website] &lt;lb&gt;
[http://megwiki.ilabs.uw.edu/ MEG Center]</text>
      <sha1>ch4osyrk20f2i5q2xv8q53boc5vykh4</sha1>
    </revision>
    <revision>
      <id>241</id>
      <parentid>240</parentid>
      <timestamp>2016-02-18T23:55:55Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="131">=Institute for Learning &amp; Brain Sciences=

*[http://ilabs.washington.edu I-LABS website]
*[http://megwiki.ilabs.uw.edu/ MEG Center]</text>
      <sha1>c87y7va08kuvckqboigs7w5nov7bhrz</sha1>
    </revision>
  </page>
  <page>
    <title>HCP Access</title>
    <ns>0</ns>
    <id>43</id>
    <revision>
      <id>264</id>
      <timestamp>2016-03-24T22:38:08Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>Created page with &quot;This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="123">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.</text>
      <sha1>psy64ft4b36vdi81hmtj8zmobf9afsk</sha1>
    </revision>
    <revision>
      <id>267</id>
      <parentid>264</parentid>
      <timestamp>2016-03-24T22:45:10Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <minor/>
      <comment>Dstrodtman moved page [[Accessing Data]] to [[HCP Access]] without leaving a redirect</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="123">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.</text>
      <sha1>psy64ft4b36vdi81hmtj8zmobf9afsk</sha1>
    </revision>
    <revision>
      <id>273</id>
      <parentid>267</parentid>
      <timestamp>2016-03-25T21:29:25Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1005">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.

==Register for an Account==
Anyone accessing data from the Human Connectome Project must have created a user account and signed the data agreement, available here:
 https://db.humanconnectome.org

==Aspera Connect==
db Human Connectome should prompt you to install Aspera Connect. Or the installation files can be found here:
 http://asperasoft.com/connect

After downloading, close your browser and run
 sh aspera-connect-[version].sh

Aspera Connect should be installed into your Applications. This will load the applet icon (a blue 'C' in white). Right click to edit preferences.
In the transfers tab, set your download location. Each subject will download as a *.zip with a corresponding *.zip.md5 file.
db Human Connectome should prompt to add security exception. If not prompted, in the security tab of your Aspera Connect preferences try adding
 aspera1.humanconnectome.org</text>
      <sha1>rjtc4mofgqbs7bwz3q6we4j8l5azftm</sha1>
    </revision>
    <revision>
      <id>299</id>
      <parentid>273</parentid>
      <timestamp>2016-06-20T17:39:54Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Aspera Connect */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1092">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.

==Register for an Account==
Anyone accessing data from the Human Connectome Project must have created a user account and signed the data agreement, available here:
 https://db.humanconnectome.org

==Aspera Connect==
db Human Connectome should prompt you to install Aspera Connect. Or the installation files can be found here:
 http://asperasoft.com/connect

Download to your ~/Downloads folder, close your browser, and in the terminal.
 cd ~/Downloads
 gunzip apera-connect-[version].tar.gz
 sh aspera-connect-[version].sh

Aspera Connect should be installed into your Applications. This will load the applet icon (a blue 'C' in white). Right click to edit preferences.
In the transfers tab, set your download location. Each subject will download as a *.zip with a corresponding *.zip.md5 file.
db Human Connectome should prompt to add security exception. If not prompted, in the security tab of your Aspera Connect preferences try adding
 aspera1.humanconnectome.org</text>
      <sha1>r5nq2u8kqd470t18zpqk5rgwromezw4</sha1>
    </revision>
    <revision>
      <id>300</id>
      <parentid>299</parentid>
      <timestamp>2016-06-20T18:49:11Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1246">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.

'''Note:''' db Human Connectome does not seem to work with Google Chrome. It is known to work for Firefox, so please use that browser to avoid problems.

==Register for an Account==
Anyone accessing data from the Human Connectome Project must have created a user account and signed the data agreement, available here:
 https://db.humanconnectome.org

==Aspera Connect==
db Human Connectome should prompt you to install Aspera Connect. Or the installation files can be found here:
 http://asperasoft.com/connect

Download to your ~/Downloads folder, close your browser, and in the terminal.
 cd ~/Downloads
 gunzip apera-connect-[version].tar.gz
 sh aspera-connect-[version].sh

Aspera Connect should be installed into your Applications. This will load the applet icon (a blue 'C' in white). Right click to edit preferences.
In the transfers tab, set your download location. Each subject will download as a *.zip with a corresponding *.zip.md5 file.
db Human Connectome should prompt to add security exception. If not prompted, in the security tab of your Aspera Connect preferences try adding
 aspera1.humanconnectome.org</text>
      <sha1>smlf6i16g27w3i3xwl9eds7usksbg22</sha1>
    </revision>
    <revision>
      <id>303</id>
      <parentid>300</parentid>
      <timestamp>2016-06-23T18:33:07Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1249">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.

'''Note:''' db Human Connectome does not seem to work with Google Chrome. It is known to work for Firefox, so please use that browser to avoid problems.

==Register for an Account==
Anyone accessing data from the Human Connectome Project must have created a user account and signed the data agreement, available here:
 https://db.humanconnectome.org

==Aspera Connect==
db Human Connectome should prompt you to install Aspera Connect. Or the installation files can be found here:
 http://asperasoft.com/connect

Download to your ~/Downloads folder, close your browser, and in the terminal.
 cd ~/Downloads
 tar -zxvf apera-connect-[version].tar.gz
 sh aspera-connect-[version].sh

Aspera Connect should be installed into your Applications. This will load the applet icon (a blue 'C' in white). Right click to edit preferences.
In the transfers tab, set your download location. Each subject will download as a *.zip with a corresponding *.zip.md5 file.
db Human Connectome should prompt to add security exception. If not prompted, in the security tab of your Aspera Connect preferences try adding
 aspera1.humanconnectome.org</text>
      <sha1>amdj3djvhu20um6vod5jxqnxtiqp06a</sha1>
    </revision>
    <revision>
      <id>304</id>
      <parentid>303</parentid>
      <timestamp>2016-06-23T19:03:02Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1637">This page will contain information about obtaining permissions and downloading data, including Aspera connect installation.

'''Note:''' db Human Connectome does not seem to work with Google Chrome. It is known to work for Firefox, so please use that browser to avoid problems.

==Register for an Account==
Anyone accessing data from the Human Connectome Project must have created a user account and signed the data agreement, available here:
 https://db.humanconnectome.org

==Aspera Connect==
db Human Connectome should prompt you to install Aspera Connect. Or the installation files can be found here:
 http://asperasoft.com/connect

Download to your ~/Downloads folder, close your browser, and in the terminal.
 cd ~/Downloads
 tar -zxvf apera-connect-[version].tar.gz
 sh aspera-connect-[version].sh

Aspera Connect should be installed into your Applications. In Mint, this will load the applet icon (a blue 'C' in white). Right click to edit preferences.
In the transfers tab, you can set a static destination for all downloaded files OR set it to prompt you where to save downloaded files. 

In Ubuntu, you will have to initiate a download before you can change your settings (the gear in the lower left corner of your Transfers - Aspera Connect window). All downloads default to your desktop, so you will not want to download a large directory until you have set your preferences.

Each subject will download as a *.zip with a corresponding *.zip.md5 file.
db Human Connectome should prompt to add security exception. If not prompted, in the security tab of your Aspera Connect preferences try adding
 aspera1.humanconnectome.org</text>
      <sha1>tf5h113g2jtgsb38r0nmhhxa306p4aa</sha1>
    </revision>
  </page>
  <page>
    <title>HCP Organization</title>
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      <comment>Created page with &quot;This section will outline how data should be organized for proper management using BDE Lab software.&quot;</comment>
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      <text xml:space="preserve" bytes="100">This section will outline how data should be organized for proper management using BDE Lab software.</text>
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      <parentid>270</parentid>
      <timestamp>2016-03-24T22:47:52Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <minor/>
      <comment>Dstrodtman moved page [[HCP Organized]] to [[HCP Organization]] without leaving a redirect</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="100">This section will outline how data should be organized for proper management using BDE Lab software.</text>
      <sha1>istbd8n3kqif0bfdfs5bto2tuvx8l67</sha1>
    </revision>
  </page>
  <page>
    <title>HCP Process</title>
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      <id>265</id>
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      <contributor>
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      <parentid>265</parentid>
      <timestamp>2016-03-24T22:45:48Z</timestamp>
      <contributor>
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        <id>5</id>
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      <minor/>
      <comment>Dstrodtman moved page [[Processing Data]] to [[HCP Process]]</comment>
      <model>wikitext</model>
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      <text xml:space="preserve" bytes="132">This page will contain information about processing HCP data. This process has been entirely automated, with proper file management.</text>
      <sha1>q0s4tulegb585jlzclc4wjbz20c3e8t</sha1>
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    <revision>
      <id>274</id>
      <parentid>268</parentid>
      <timestamp>2016-03-25T21:35:58Z</timestamp>
      <contributor>
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        <id>5</id>
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      <model>wikitext</model>
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      <text xml:space="preserve" bytes="381">This page will contain information about processing HCP data. This process has been entirely automated, with proper file management.

==HCP Extension for AFQ==
Preprocessing of HCP diffusion data for AFQ has been fully automated. Read Matlab help documentation for further explanation
 HCP_run_dtiInit
Fixes x flip of bvecs and rounds small bvals (&lt;10) to 0 to comply with dtiInit.</text>
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    <revision>
      <id>275</id>
      <parentid>274</parentid>
      <timestamp>2016-03-25T21:44:47Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
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      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="554">This page will contain information about processing HCP data. This process has been entirely automated, with proper file management.

==HCP Extension for AFQ==
Preprocessing of HCP diffusion data for AFQ has been fully automated. Output will increase the file directory size by approximately 50% (resulting in around 2 TB for the 900 subjects diffusion data) Read Matlab help documentation for further explanation
 HCP_run_dtiInit
Fixes x flip of bvecs and rounds small bvals (&lt;10) to 0 to comply with dtiInit.

==AFQ for HCP==
Instruction forthcoming...</text>
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      <id>276</id>
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      <text xml:space="preserve" bytes="409">Software to process HCP data through AFQ is available from https://github.com/yeatmanlab/BrainTools/tree/master/projects/HCP 

==HCP Extension for AFQ==
Processing of HCP diffusion data through AFQ has been fully automated. Output will result in a file directory roughly twice as large  (resulting in around 2.5 TB for the 900 subjects diffusion data [3.6 TB if zipped files left on drive]).

 HCP_run_dtiInit</text>
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  </page>
  <page>
    <title>Helpful Links</title>
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      <text xml:space="preserve" bytes="77">Here are some helpful resources for the methods and information in this Wiki:</text>
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  </page>
  <page>
    <title>ILABS Brain Seminar</title>
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      <timestamp>2015-10-23T23:30:33Z</timestamp>
      <contributor>
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        <id>1</id>
      </contributor>
      <comment>Created page with &quot;'''October 29 - Jason Yeatman''' Neuron. 2007 Jul 5;55(1):143-56. Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual...&quot;</comment>
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      <text xml:space="preserve" bytes="1599">'''October 29 - Jason Yeatman''' Neuron. 2007 Jul 5;55(1):143-56. Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L.
Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

'''November 5 -''' No Brain Seminar

'''November 12 - Open''' 

'''November 19 - Open'''

'''November 26 - Thanksgiving''

'''December 3 - Mark Wronkiewicz'''</text>
      <sha1>83c4z13frwb15d90d4z0famipz2u04d</sha1>
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    <revision>
      <id>76</id>
      <parentid>75</parentid>
      <timestamp>2015-10-23T23:31:20Z</timestamp>
      <contributor>
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        <id>1</id>
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      <text xml:space="preserve" bytes="1601">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

'''November 5 -''' No Brain Seminar

'''November 12 - Open''' 

'''November 19 - Open'''

'''November 26 - Thanksgiving''

'''December 3 - Mark Wronkiewicz'''</text>
      <sha1>0qp1dczsl9ejyclupd35svsokc2ah2s</sha1>
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    <revision>
      <id>77</id>
      <parentid>76</parentid>
      <timestamp>2015-10-23T23:31:37Z</timestamp>
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      <text xml:space="preserve" bytes="1602">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

'''November 5 -''' No Brain Seminar

'''November 12 - Open''' 

'''November 19 - Open'''

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz'''</text>
      <sha1>1z4dsdh211s36tfgpg2grj5jxlsd11y</sha1>
    </revision>
    <revision>
      <id>78</id>
      <parentid>77</parentid>
      <timestamp>2015-10-24T19:22:13Z</timestamp>
      <contributor>
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        <id>1</id>
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      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1839">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 -''' No Brain Seminar

'''November 12 - Open''' 

'''November 19 - Open'''

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Something cool with decoding and single trial MEG analysis</text>
      <sha1>ipupl43jy31h54toop8w6s7w2waq60n</sha1>
    </revision>
    <revision>
      <id>79</id>
      <parentid>78</parentid>
      <timestamp>2015-10-27T17:36:05Z</timestamp>
      <contributor>
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      <model>wikitext</model>
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      <text xml:space="preserve" bytes="2356">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 -''' No Brain Seminar

'''November 12 - Open''' 

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Something cool with decoding and single trial MEG analysis</text>
      <sha1>dp5i9i5ecem5ahgwt7t6cxigs3w3bjk</sha1>
    </revision>
    <revision>
      <id>80</id>
      <parentid>79</parentid>
      <timestamp>2015-10-27T18:16:46Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
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      <model>wikitext</model>
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      <text xml:space="preserve" bytes="2729">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 -''' No Brain Seminar

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Something cool with decoding and single trial MEG analysis</text>
      <sha1>hp7q0dvrftda8ra3jis703nnqo0vhf0</sha1>
    </revision>
    <revision>
      <id>81</id>
      <parentid>80</parentid>
      <timestamp>2015-10-28T18:08:54Z</timestamp>
      <contributor>
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        <id>1</id>
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      <model>wikitext</model>
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      <text xml:space="preserve" bytes="2730">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Something cool with decoding and single trial MEG analysis</text>
      <sha1>aj7znkbvov2richn92r8ebi8ljusqhj</sha1>
    </revision>
    <revision>
      <id>82</id>
      <parentid>81</parentid>
      <timestamp>2015-10-28T18:10:34Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2805">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Something cool with decoding and single trial MEG analysis

'''December 10 - Patrick Donnelly''' RAVE-O Reading Intervention Program.</text>
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    </revision>
    <revision>
      <id>133</id>
      <parentid>82</parentid>
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      <text xml:space="preserve" bytes="2868">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Something cool with decoding and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' RAVE-O Reading Intervention Program.</text>
      <sha1>8imdxx0yasnv1ur5dgnefi2x6pmoeat</sha1>
    </revision>
    <revision>
      <id>209</id>
      <parentid>133</parentid>
      <timestamp>2015-12-15T20:16:28Z</timestamp>
      <contributor>
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      <text xml:space="preserve" bytes="5569">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Brain computer interface and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' Dyslexia interventions targeting multiple components of reading. 
Annu Rev Psychol. 2012;63:427-52. doi: 10.1146/annurev-psych-120710-100431. Epub 2011 Aug 11.Paperpile
Rapid automatized naming (RAN) and reading fluency: implications for understandingand treatment of reading disabilities.
Norton ES1, Wolf M.
Author information
Abstract
Fluent reading depends on a complex set of cognitive processes that must work together in perfect concert. Rapidautomatized naming (RAN) tasks provide insight into this system, acting as a microcosm of the processes involved in reading. In this review, we examine both RAN and reading fluency and how each has shaped our understandingof reading disabilities. We explore the research that led to our current understanding of the relationships betweenRAN and reading and what makes RAN unique as a cognitive measure. We explore how the automaticity that supports RAN affects reading across development, reading abilities, and languages, and the biological bases of these processes. Finally, we bring these converging areas of knowledge together by examining what the collective studies of RAN and reading fluency contribute to our goals of creating optimal assessments and interventions that help every child become a fluent, comprehending reader.
J Learn Disabil. 2012 Mar-Apr;45(2):99-127. doi: 10.1177/0022219409355472. Epub 2010 May 5.Paperpile
Multiple-component remediation for developmental reading disabilities: IQ,socioeconomic status, and race as factors in remedial outcome.
Morris RD1, Lovett MW2, Wolf M3, Sevcik RA1, Steinbach KA4, Frijters JC5, Shapiro MB1.
Author information
Abstract
Results from a controlled evaluation of remedial reading interventions are reported: 279 young disabled readers were randomly assigned to a program according to a 2 × 2 × 2 factorial design (IQ, socioeconomic status [SES], and race). The effectiveness of two multiple-component intervention programs for children with reading disabilities(PHAB + RAVE-O; PHAB + WIST) was evaluated against alternate (CSS, MATH) and phonological control programs. Interventions were taught an hour daily for 70 days on a 1:4 ratio at three different sites. Multiple-component programs showed significant improvements relative to control programs on all basic reading skills after 70 hours and at 1-year follow-up. Equivalent gains were observed for different racial, SES, and IQ groups. Thesefactors did not systematically interact with program. Differential outcomes for word identification, fluency, comprehension, and vocabulary were found between the multidimensional programs, although equivalent long-term outcomes and equal continued growth confirmed that different pathways exist to effective readingremediation.</text>
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    <revision>
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      <text xml:space="preserve" bytes="3663">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Brain computer interface and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' Dyslexia interventions targeting multiple components of reading. 
Annu Rev Psychol. 2012;63:427-52. doi: 10.1146/annurev-psych-120710-100431. Epub 2011 Aug 11.Paperpile
Rapid automatized naming (RAN) and reading fluency: implications for understandingand treatment of reading disabilities.
Norton ES1, Wolf M.
J Learn Disabil. 2012 Mar-Apr;45(2):99-127. doi: 10.1177/0022219409355472. Epub 2010 May 5.Paperpile
Multiple-component remediation for developmental reading disabilities: IQ,socioeconomic status, and race as factors in remedial outcome.
Morris RD1, Lovett MW2, Wolf M3, Sevcik RA1, Steinbach KA4, Frijters JC5, Shapiro MB1.

'''December 24 - No Brain Seminar'''

'''December 31 - No Brain Seminar'''

'''January 7 - No Brain Seminar'''

'''January 14 - No Brain Seminar'''

'''January 21 - Samu Taulu''' New MEG developments</text>
      <sha1>n2aa0vmokfrwe0z6hp7qnqifgb8ys5r</sha1>
    </revision>
    <revision>
      <id>211</id>
      <parentid>210</parentid>
      <timestamp>2015-12-17T01:21:54Z</timestamp>
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      <text xml:space="preserve" bytes="3704">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Brain computer interface and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' Dyslexia interventions targeting multiple components of reading. 
Annu Rev Psychol. 2012;63:427-52. doi: 10.1146/annurev-psych-120710-100431. Epub 2011 Aug 11.Paperpile
Rapid automatized naming (RAN) and reading fluency: implications for understandingand treatment of reading disabilities.
Norton ES1, Wolf M.
J Learn Disabil. 2012 Mar-Apr;45(2):99-127. doi: 10.1177/0022219409355472. Epub 2010 May 5.Paperpile
Multiple-component remediation for developmental reading disabilities: IQ,socioeconomic status, and race as factors in remedial outcome.
Morris RD1, Lovett MW2, Wolf M3, Sevcik RA1, Steinbach KA4, Frijters JC5, Shapiro MB1.

'''December 24 - No Brain Seminar'''

'''December 31 - No Brain Seminar'''

'''January 7 - OPEN'''

'''January 14 - OPEN'''

'''January 21 - Samu Taulu''' New MEG developments

'''January 28 - Caitlin Hudac''' Autism, eye movements and ERPs</text>
      <sha1>od5eay1frkmlej5bo4kg0t09nnqvov2</sha1>
    </revision>
    <revision>
      <id>213</id>
      <parentid>211</parentid>
      <timestamp>2015-12-17T23:16:57Z</timestamp>
      <contributor>
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        <id>1</id>
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      <model>wikitext</model>
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      <text xml:space="preserve" bytes="3826">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Brain computer interface and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' Dyslexia interventions targeting multiple components of reading. 
Annu Rev Psychol. 2012;63:427-52. doi: 10.1146/annurev-psych-120710-100431. Epub 2011 Aug 11.Paperpile
Rapid automatized naming (RAN) and reading fluency: implications for understandingand treatment of reading disabilities.
Norton ES1, Wolf M.
J Learn Disabil. 2012 Mar-Apr;45(2):99-127. doi: 10.1177/0022219409355472. Epub 2010 May 5.Paperpile
Multiple-component remediation for developmental reading disabilities: IQ,socioeconomic status, and race as factors in remedial outcome.
Morris RD1, Lovett MW2, Wolf M3, Sevcik RA1, Steinbach KA4, Frijters JC5, Shapiro MB1.

'''December 24 - No Brain Seminar'''

'''December 31 - No Brain Seminar'''

'''January 7 - Christina Zhao''' EEG project looking at how tempo and temporal structure type influence temporal structure processing in adults.

'''January 14 - OPEN'''

'''January 21 - Samu Taulu''' New MEG developments

'''January 28 - Caitlin Hudac''' Autism, eye movements and ERPs</text>
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    <revision>
      <id>214</id>
      <parentid>213</parentid>
      <timestamp>2015-12-18T01:09:36Z</timestamp>
      <contributor>
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        <id>1</id>
      </contributor>
      <model>wikitext</model>
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      <text xml:space="preserve" bytes="4286">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Brain computer interface and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' Dyslexia interventions targeting multiple components of reading. 
Annu Rev Psychol. 2012;63:427-52. doi: 10.1146/annurev-psych-120710-100431. Epub 2011 Aug 11.Paperpile
Rapid automatized naming (RAN) and reading fluency: implications for understandingand treatment of reading disabilities.
Norton ES1, Wolf M.
J Learn Disabil. 2012 Mar-Apr;45(2):99-127. doi: 10.1177/0022219409355472. Epub 2010 May 5.Paperpile
Multiple-component remediation for developmental reading disabilities: IQ,socioeconomic status, and race as factors in remedial outcome.
Morris RD1, Lovett MW2, Wolf M3, Sevcik RA1, Steinbach KA4, Frijters JC5, Shapiro MB1.

'''December 24 - No Brain Seminar'''

'''December 31 - No Brain Seminar'''

'''January 7 - Christina Zhao''' EEG project looking at how tempo and temporal structure type influence temporal structure processing in adults.

'''January 14 - OPEN'''

'''January 21 - Samu Taulu''' New MEG developments

'''January 28 - Caitlin Hudac''' ''The eyes have it: Potential uses for the integration of eye tracking (ET) and single-trial MEG/EEG.'' I will describe my recent work using single-trial ERP and EEG analyses to describe signal habituation of the social brain in autism. However, it is important to consider how dynamic behavioral changes (e.g., areas of visual attention) may relate to reduced social brain responses. I will propose a potential new project integrating ET-MEG and/or ET-EEG and seek feedback from the group.</text>
      <sha1>swq4pisn80mz1o2yfc44420nie2r9px</sha1>
    </revision>
    <revision>
      <id>223</id>
      <parentid>214</parentid>
      <timestamp>2016-01-12T22:23:59Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
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      <text xml:space="preserve" bytes="4520">'''October 29 - Jason Yeatman''' Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. Vinckier F1, Dehaene S, Jobert A, Dubus JP, Sigman M, Cohen L. Neuron. 2007 Jul 5;55(1):143-56. 

Abstract
Visual word recognition has been proposed to rely on a hierarchy of increasingly complex neuronal detectors, from individual letters to bigrams and morphemes. We used fMRI to test whether such a hierarchy is present in the left occipitotemporal cortex, at the site of the visual word-form area, and with an anterior-to-posterior progression. We exposed adult readers to (1) false-font strings; (2) strings of infrequent letters; (3) strings of frequent letters but rare bigrams; (4) strings with frequent bigrams but rare quadrigrams; (5) strings with frequent quadrigrams; (6) real words. A gradient of selectivity was observed through the entire span of the occipitotemporal cortex, with activation becoming more selective for higher-level stimuli toward the anterior fusiform region. A similar gradient was also seen in left inferior frontoinsular cortex. Those gradients were asymmetrical in favor of the left hemisphere. We conclude that the left occipitotemporal visual word-form area, far from being a homogeneous structure, presents a high degree of functional and spatial hierarchical organization which must result from a tuning process during reading acquisition.

A good related paper for background:
Binder, Jeffrey R., et al. &quot;Tuning of the human left fusiform gyrus to sublexical orthographic structure.&quot; Neuroimage 33.2 (2006): 739-748.

'''November 5 - No Brain Seminar''' 

'''November 12 - Ross Maddox''' Tanner, Darren, Kara Morgan‐Short, and Steven J. Luck. &quot;How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition.&quot; Psychophysiology (2015). http://www.ncbi.nlm.nih.gov/pubmed/25903295
This will be an informal discussion of these issues so please read the paper and plan to participate

'''November 19 - Ariel Rokem &amp; Jason Yeatman''' Data sharing. Scientific transparency and reproducibility has become a major worry among scientists across disciplines and has also seen a lot of recent media attention. In response to these concerns, funding agencies and journals have been revising their policies on making published data openly available. We will lead a discussion on (1) best practices in data sharing, (2) resources that support and facilitate data sharing, (3) what data sharing means for the careers of young scientists.

'''November 26 - Thanksgiving'''

'''December 3 - Mark Wronkiewicz''' Brain computer interface and single trial MEG analysis

'''December 10 - Alex White''' Divided attention and reading.

'''December 17 - Patrick Donnelly''' Dyslexia interventions targeting multiple components of reading. 
Annu Rev Psychol. 2012;63:427-52. doi: 10.1146/annurev-psych-120710-100431. Epub 2011 Aug 11.Paperpile
Rapid automatized naming (RAN) and reading fluency: implications for understandingand treatment of reading disabilities.
Norton ES1, Wolf M.
J Learn Disabil. 2012 Mar-Apr;45(2):99-127. doi: 10.1177/0022219409355472. Epub 2010 May 5.Paperpile
Multiple-component remediation for developmental reading disabilities: IQ,socioeconomic status, and race as factors in remedial outcome.
Morris RD1, Lovett MW2, Wolf M3, Sevcik RA1, Steinbach KA4, Frijters JC5, Shapiro MB1.

'''December 24 - No Brain Seminar'''

'''December 31 - No Brain Seminar'''

'''January 7 - Christina Zhao''' EEG project looking at how tempo and temporal structure type influence temporal structure processing in adults.

'''January 14 - OPEN'''

'''January 21 - Samu Taulu''' Brief review of basic MEG physics as a basis for new developments
- Why is the distance of the head to the MEG array so important
- What is the time resolution of MEG based on
- Why we shouldn't apply (inverse) models that are too complex for infant data

'''January 28 - Caitlin Hudac''' ''The eyes have it: Potential uses for the integration of eye tracking (ET) and single-trial MEG/EEG.'' I will describe my recent work using single-trial ERP and EEG analyses to describe signal habituation of the social brain in autism. However, it is important to consider how dynamic behavioral changes (e.g., areas of visual attention) may relate to reduced social brain responses. I will propose a potential new project integrating ET-MEG and/or ET-EEG and seek feedback from the group.</text>
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    </revision>
  </page>
  <page>
    <title>MEG Data Acquisition</title>
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      <comment>Created page with &quot;1. After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.   cd /home/jyeatman/git/mnefun/bin   python run_mne_bem.py --subject NLR_201_GS --lay...&quot;</comment>
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      <text xml:space="preserve" bytes="192">1. After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite</text>
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      <timestamp>2015-10-23T19:14:25Z</timestamp>
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      <text xml:space="preserve" bytes="288">1. After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

2. To visualize source localized data
 mne_analyze

3. File -&gt; Load Surface -&gt; Select Inflated</text>
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      <text xml:space="preserve" bytes="326">1. After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

2. To visualize source localized data
 mne_analyze

3. File -&gt; Load Surface -&gt; Select Inflated

[[File:LoadInverse-mne analyze.png]]</text>
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      <text xml:space="preserve" bytes="342">1. After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

2. To visualize source localized data
 mne_analyze

3. File -&gt; Load Surface -&gt; Select Inflated

4. File-&gt; Open

[[File:LoadInverse-mne analyze.png]]</text>
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      <text xml:space="preserve" bytes="875">1. After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

2. Coordinate alignment.
  mne_analyze
File -&gt; Load Surface -&gt; Select Inflated
File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Adjust -&gt; Coordinate Alignment
View -&gt; Show viewer
Click Options -&gt; check Digitizer Data and HPI and landmarks only, 
Click each Fiducial location and then click &quot;Align using fiducials&quot;
Save Default
Make a &quot;trans&quot; folder within the subject's directory
Move transform file and rename subj-trans.fif

2. To visualize source localized data
 mne_analyze

3. File -&gt; Load Surface -&gt; Select Inflated

4. File-&gt; Open

[[File:LoadInverse-mne analyze.png]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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      <parentid>59</parentid>
      <timestamp>2015-10-23T22:15:05Z</timestamp>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png]]
Click Options -&gt; check Digitizer Data and HPI and landmarks only, 
Click each Fiducial location and then click &quot;Align using fiducials&quot;
Save Default
Make a &quot;trans&quot; folder within the subject's directory
Move transform file and rename subj-trans.fif

2. To visualize source localized data
 mne_analyze

3. File -&gt; Load Surface -&gt; Select Inflated

4. File-&gt; Open

[[File:LoadInverse-mne analyze.png]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png]]
Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png]]
From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|center]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png]]
From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|center]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|50px|center]]
From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|center]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|200px|center]]
From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|200px|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|300px|center]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|300px|center]]
From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|400px|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|300px|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|400px|right]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|300px|center]]
From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|400px|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|300px|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|400px|right]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|300px|right]]

From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|400px|right]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|300px|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|400px|left]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|300px|left]]

From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|400px|left]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|300px|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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      <parentid>71</parentid>
      <timestamp>2015-10-23T22:32:56Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2165">
== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|400px|center]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|300px|center]]

From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|400px|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space ==
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|300px|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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    </revision>
    <revision>
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      <parentid>72</parentid>
      <timestamp>2015-10-23T22:33:48Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2172">__TOC__
== Creating a BEM model for source localization ==

After running freesurfer on a subject's T1 anatomy we next need to create a BEM model.
  cd /home/jyeatman/git/mnefun/bin
  python run_mne_bem.py --subject NLR_201_GS --layers 1 --overwrite

== Aligning MEG sensor data to the BEM model ==
Open mne_analyze to compute the coordinate alignment. In mne_analyze load the subject's surface and digitizer data:
  File -&gt; Load Surface -&gt; Select Inflated
  File -&gt; Load Digitizer Data -&gt; sss_fif -&gt; select any raw file
Next adjust how these digitized points align with the scalp in the MRI
  Adjust -&gt; Coordinate Alignment
  View -&gt; Show viewer
Within the viewer window click the &quot;Options&quot; button. Within the &quot;Viewer Options&quot; dialogue check &quot;digitizer data&quot; and &quot;HPI and landmarks only&quot;. [[File:Viewer options.png|400px|center]]

Next mark the fiducial locations on the scalp. To do this click LAP, RAP and Nasion in the &quot;Adjust coordinate alignment&quot; dialogue and then mark each spot by clicking on the scalp surface . After marking each location click &quot;Align using fiducials&quot;. [[File:Mne analyze AdjustCoordAlign.png|300px|center]]

From this point it is an art of getting as many of the digitizer points as possible to lie on the scalp. In the &quot;Viewer Options&quot; dialogue make the scalp transparent and un-check the &quot;HPI and landmarks only&quot; button. This will let you see where each digitized point lies with respect to the scalp. You want each point on the surface but not below it. Blue points are below the surface. Adjust points manually with the arrow buttons and using the ICP algorithm until you are happy. [[File:Mne analyze viewer.png|400px|center]]

Once you have aligned everything click &quot;Save Default&quot; in the coodinate adjustment dialogue. This will save out the transform in the subjects folder. Then, make a &quot;trans&quot; folder within the subject's directory and move transform file and rename subj-trans.fif

== Visualizing data in source space ==
  mne_analyze
  File -&gt; Load Surface -&gt; Select Inflated
  File-&gt; Open

[[File:LoadInverse-mne analyze.png|300px|center]]
To adjust sensor plots: Adjust -&gt; Scales
To adjust source visualization: Adjust -&gt; Estimates</text>
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  </page>
  <page>
    <title>MRI Data Acquisition</title>
    <ns>0</ns>
    <id>6</id>
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      <timestamp>2015-09-01T19:03:33Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:  /mnt/diskArray/projects/MRI/study...&quot;</comment>
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      <text xml:space="preserve" bytes="330">After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:
 /mnt/diskArray/projects/MRI/study_sID_initials
 /mnt/diskArray/projects/MRI/NLR_001_JY
Next we run the [[Anatomy|anatomical]] and [[Diffusion|diffusion]] data through our typical pipeline.</text>
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    <revision>
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      <parentid>31</parentid>
      <timestamp>2015-09-01T19:04:37Z</timestamp>
      <contributor>
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        <id>1</id>
      </contributor>
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      <text xml:space="preserve" bytes="353">After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:
 /mnt/diskArray/projects/MRI/study_sID_initials
 /mnt/diskArray/projects/MRI/NLR_001_JY
Next we run the [[Anatomy Pipeline]] and [[Diffusion Pipeline]] to preprocess this data that will be used for many purposes.</text>
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    <revision>
      <id>34</id>
      <parentid>32</parentid>
      <timestamp>2015-09-01T19:07:03Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="364">After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:
 /mnt/diskArray/projects/MRI/study_sID_initials_session#
 /mnt/diskArray/projects/MRI/NLR_001_JY_1
Next we run the [[Anatomy Pipeline]] and [[Diffusion Pipeline]] to preprocess this data that will be used for many purposes.</text>
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      <parentid>34</parentid>
      <timestamp>2015-09-01T19:08:31Z</timestamp>
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        <id>1</id>
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      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="377">After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:
 /mnt/diskArray/projects/MRI/study_sID_initials/YearMonthDay/
 /mnt/diskArray/projects/MRI/NLR_001_JY/20150926/
Next we run the [[Anatomy Pipeline]] and [[Diffusion Pipeline]] to preprocess this data that will be used for many purposes.</text>
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    <revision>
      <id>83</id>
      <parentid>35</parentid>
      <timestamp>2015-10-28T21:18:38Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="396">After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:
 /mnt/diskArray/projects/MRI/study_sID_initials/YearMonthDay/raw
For example:
 /mnt/diskArray/projects/MRI/NLR_001_JY/20150926/raw
Next we run the [[Anatomy Pipeline]] and [[Diffusion Pipeline]] to preprocess this data that will be used for many purposes.</text>
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    </revision>
    <revision>
      <id>84</id>
      <parentid>83</parentid>
      <timestamp>2015-10-28T21:21:46Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="859">After MRI data has been acquired at the DISC MRI center it should be saved as PAR/REC files. For each subject, the data should be placed in:
 /mnt/diskArray/projects/MRI/study_sID_initials/YearMonthDay/raw
For example:
 /mnt/diskArray/projects/MRI/NLR_001_JY/20150926/raw
When a subject has multiple scan sessions, they will go in separate folders within the subject's main folder, each with the date of the scan, so that we can determine the order of the scans. All the raw data should be within the raw folder so that the outputs of different processing stages can be saved in separate named folders. For example:
 /mnt/diskArray/projects/MRI/NLR_001_JY/20150926/dti96trilinrt
 /mnt/diskArray/projects/MRI/NLR_001_JY/20150926/mrtrix
Next we run the [[Anatomy Pipeline]] and [[Diffusion Pipeline]] to preprocess this data that will be used for many purposes.</text>
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  </page>
  <page>
    <title>Main Page</title>
    <ns>0</ns>
    <id>1</id>
    <revision>
      <id>1</id>
      <timestamp>2015-08-13T18:37:13Z</timestamp>
      <contributor>
        <username>MediaWiki default</username>
        <id>0</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="592">&lt;strong&gt;MediaWiki has been successfully installed.&lt;/strong&gt;

Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software.

== Getting started ==
* [//www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]
* [//www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]
* [//www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]</text>
      <sha1>glba3g2evzm40dqnqxegze66eqibkvb</sha1>
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    <revision>
      <id>2</id>
      <parentid>1</parentid>
      <timestamp>2015-08-13T18:45:14Z</timestamp>
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        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="588">&lt;strong&gt;Brain Development &amp; Education Lab Wiki&lt;/strong&gt;

Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software.

== Getting started ==
* [//www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]
* [//www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]
* [//www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]</text>
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    </revision>
  </page>
  <page>
    <title>NFS</title>
    <ns>0</ns>
    <id>52</id>
    <revision>
      <id>314</id>
      <timestamp>2016-07-27T23:02:51Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>Created page with &quot;Mounting all of our scratch space using NFS will allow us to access each other's files as if they are on the local machine, reducing redundancy and maximizing our storage. Als...&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="764">Mounting all of our scratch space using NFS will allow us to access each other's files as if they are on the local machine, reducing redundancy and maximizing our storage. Also, this will make things much easier once the cluster is up and running.

==Install NFS Server==
To check if you've already installed the NFS server
 dpkg -l | grep nfs-kernel-server
If not
 sudo apt-get install nfs-kernel-server

==Make and Export Local Drives==
Our naming convention dictates you enter the name of your computer followed by a number if you have more than 1 spinny drive
 sudo mkdir -p /export/&lt;computername&gt;
Add the following the /etc/fstab file to export drive at boot up.
 /mnt/scratch    /export/&lt;computername&gt;   none    bind  0  0


==This page is not yet complete==</text>
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  </page>
  <page>
    <title>Processing Data</title>
    <ns>0</ns>
    <id>45</id>
    <redirect title="HCP Process" />
    <revision>
      <id>269</id>
      <timestamp>2016-03-24T22:45:48Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>Dstrodtman moved page [[Processing Data]] to [[HCP Process]]</comment>
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      <text xml:space="preserve" bytes="25">#REDIRECT [[HCP Process]]</text>
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  </page>
  <page>
    <title>Psychophysics</title>
    <ns>0</ns>
    <id>18</id>
    <revision>
      <id>135</id>
      <timestamp>2015-11-17T16:39:26Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;__TOC__ ==Attention: Spatial Cueing==&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="37">__TOC__
==Attention: Spatial Cueing==</text>
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    </revision>
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      <parentid>135</parentid>
      <timestamp>2015-11-17T19:41:29Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="208">__TOC__
==Attention: Spatial Cueing==
 Preparation:
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test

===Procedure===</text>
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    <revision>
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      <parentid>136</parentid>
      <timestamp>2015-11-18T22:53:06Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Procedure */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1349">__TOC__
==Attention: Spatial Cueing==
 Preparation:
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test

===Procedure===
 1. Load MatLab.  Navigate to the Code directory
     cd /.../code
 2. Explain the task. Below is an example script.
 
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]</text>
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    <revision>
      <id>144</id>
      <parentid>139</parentid>
      <timestamp>2015-11-18T23:36:52Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Procedure */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4788">__TOC__
==Attention: Spatial Cueing==
 Preparation:
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test

===Procedure===
 1. Load MatLab.  Navigate to the Code directory
     cd /.../code
 2. Introduce the task
 3. Practice Uncued task
 4. Introduce the Cued
 5. Practice the Cued
 6. Introduce the Single Stimulus
 7. Practice the Single Stimulus
 8. Additional Practice
 9. Run the Series

====Introduction==== 
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

====Uncued Practice====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

====Introduce the Cued task====
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

====Cued Practice====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

====Introduce the Single Stimulus====
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

====Practice Single Stimulus====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

====Additional Practice====
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.</text>
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        <id>2</id>
      </contributor>
      <comment>/* Procedure */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4581">__TOC__
==Attention: Spatial Cueing==
 Preparation:
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test

===Procedure===
 Load MatLab.  Navigate to the Code directory
   cd /.../code


====Introduction==== 
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

====Uncued Practice====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

====Introduce the Cued task====
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

====Cued Practice====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

====Introduce the Single Stimulus====
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

====Practice Single Stimulus====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

====Additional Practice====
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.</text>
      <sha1>lgcll6wp2sq18swth9r0pfmjqq1ex8i</sha1>
    </revision>
    <revision>
      <id>146</id>
      <parentid>145</parentid>
      <timestamp>2015-11-18T23:45:12Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4917">__TOC__
==Attention: Spatial Cueing==
 Preparation:
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test

===Procedure===
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;


====Introduction==== 
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

====Uncued Practice====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

====Introduce the Cued task====
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

====Cued Practice====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

====Introduce the Single Stimulus====
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

====Practice Single Stimulus====
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

====Additional Practice====
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

====Run the Series====
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>g3fftq6xzn5kg95ia4dwc9xqtjx6bla</sha1>
    </revision>
    <revision>
      <id>147</id>
      <parentid>146</parentid>
      <timestamp>2015-11-18T23:46:25Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4883">__TOC__
==Attention: Spatial Cueing==
 Preparation:
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;


===Introduction===
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>6fft2evp7kpsr91ytjr5zhyjga4zm2g</sha1>
    </revision>
    <revision>
      <id>148</id>
      <parentid>147</parentid>
      <timestamp>2015-11-18T23:47:00Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4868">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>lyfsddaf4yofyrnocxffl4i27eknctw</sha1>
    </revision>
    <revision>
      <id>150</id>
      <parentid>148</parentid>
      <timestamp>2015-11-18T23:50:16Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5048">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg | 200px | thumb]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>ddgv15fknnfa04gt18jo777s1pxhnu4</sha1>
    </revision>
    <revision>
      <id>151</id>
      <parentid>150</parentid>
      <timestamp>2015-11-18T23:50:53Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5032">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg]]
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>q06uzxekisy8ndwqa149uc2t0d5gax7</sha1>
    </revision>
    <revision>
      <id>152</id>
      <parentid>151</parentid>
      <timestamp>2015-11-18T23:51:51Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5134">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this game you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>4wc7ine24w0jjywp6sv9zxtnqxxay2g</sha1>
    </revision>
    <revision>
      <id>153</id>
      <parentid>152</parentid>
      <timestamp>2015-11-18T23:54:18Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5134">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is off kilter.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>468v16fbmx4statbm443cahr33iiw2w</sha1>
    </revision>
    <revision>
      <id>154</id>
      <parentid>153</parentid>
      <timestamp>2015-11-19T00:30:55Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5148">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|300px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>jorzgy8v8lxzaxpx566u6gsmo8qbmhy</sha1>
    </revision>
    <revision>
      <id>155</id>
      <parentid>154</parentid>
      <timestamp>2015-11-19T00:31:18Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5148">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg]]
Or this:
 [[File:CueGaborsBigTilt2.jpg]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>naxpsgubdd40mb7jl4dmzfbte2dmcjp</sha1>
    </revision>
    <revision>
      <id>156</id>
      <parentid>155</parentid>
      <timestamp>2015-11-19T00:42:43Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5176">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
[File:Gabors1.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
[[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
[[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
[[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
[[File:CueGabors3.jpg|800px]]
Or this:
[[File:CueGaborsBigTilt2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
[[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>b4uctip5zuuqw2sd3wr1e44b6s0lzjz</sha1>
    </revision>
    <revision>
      <id>157</id>
      <parentid>156</parentid>
      <timestamp>2015-11-19T00:43:23Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5183">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [File:Gabors1.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg|800px]]
Or this:
 [[File:CueGaborsBigTilt2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>7xcp25yb061n4a5xnoehvhbr0bg0tqm</sha1>
    </revision>
    <revision>
      <id>158</id>
      <parentid>157</parentid>
      <timestamp>2015-11-19T17:41:07Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5184">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg|800px]]
Or this:
 [[File:CueGaborsBigTilt2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>2lwzruym7szbm9clcj0f1z5aq5ui7w7</sha1>
    </revision>
    <revision>
      <id>160</id>
      <parentid>158</parentid>
      <timestamp>2015-11-19T17:48:30Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5334">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the blurred circles?
 [[File:GaborsCloseUp.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabors3.jpg|800px]]
Or this:
 [[File:CueGaborsBigTilt2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>huvewvs4g3zr58gsnz1qova4m5rvip1</sha1>
    </revision>
    <revision>
      <id>167</id>
      <parentid>160</parentid>
      <timestamp>2015-11-19T18:49:56Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduce the Cued task */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5324">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the blurred circles?
 [[File:GaborsCloseUp.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CueGabor.jpg|800px]]
Or this:
 [[File:CueGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>js3fyuayp2gg02taowjc6o7sanfs9ut</sha1>
    </revision>
    <revision>
      <id>168</id>
      <parentid>167</parentid>
      <timestamp>2015-11-19T18:50:48Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduce the Cued task */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5326">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the blurred circles?
 [[File:GaborsCloseUp.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>86qcr5kllboqe6qi78h4vea0sllsj7o</sha1>
    </revision>
    <revision>
      <id>170</id>
      <parentid>168</parentid>
      <timestamp>2015-11-19T18:54:38Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5355">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the blurred circles?
 [[File:GaborsCloseUp.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red + in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>hl0thyzwl50lq9iccs1w6rxdzuvj3id</sha1>
    </revision>
    <revision>
      <id>171</id>
      <parentid>170</parentid>
      <timestamp>2015-11-19T18:58:35Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5359">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the blurred circles?
 [[File:GaborsCloseUp.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the blurred circle that is tilted to one side.  
As you can see all eight of the circles are the same except for one.  For each task in the game you will need to find the one that is shifted like this* or like this*
*During demonstration, use your hand to show the tilt of the blurred lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the one that is different, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the blurred lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are shifted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the blurred circle is on, so even though the different circle is on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which circle is not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the blurry circle that will be different.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are pointing - the only difference is that you won't have to figure out which one it is that is different.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one of the blurry circles on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurred lines are pointing.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and intialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>bn1mwd8rqzvx6c4mne4mf7u38i9eh0n</sha1>
    </revision>
    <revision>
      <id>172</id>
      <parentid>171</parentid>
      <timestamp>2015-11-19T21:07:29Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5260">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the lines?
 [[File:GaborsCloseUp.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the lines that is tilted to the side.  
As you can see all eight of the lines are the same except for one.  For each task in the game you will need to find the one that is tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted lines, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted lines are on, so even if the lines are on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which lines are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the lines that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are tilted - the only difference is that you won't have to figure out which one it is.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of lines on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry lines are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>kivfqbnky7xm9un6ytb8g8qjtfvdgli</sha1>
    </revision>
    <revision>
      <id>173</id>
      <parentid>172</parentid>
      <timestamp>2015-11-19T21:09:40Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5266">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally a blurry.  Do you notice anything about the lines?
 [[File:GaborsCloseUp|800px.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the lines that is tilted to the side.  
As you can see all eight of the lines are the same except for one.  For each task in the game you will need to find the one that is tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the lines, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted lines, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the lines are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the lines are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted lines are on, so even if the lines are on the right side of the screen, you would still press the Left Arrow since the tops of the blurred lines are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the lines are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which lines are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the lines that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the lines are tilted - the only difference is that you won't have to figure out which one it is.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of lines on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry lines are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>7qzcnyjfozvgqm27nz99n745yo6ob6f</sha1>
    </revision>
    <revision>
      <id>174</id>
      <parentid>173</parentid>
      <timestamp>2015-11-19T21:14:57Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5286">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp|800px.jpg]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which lines are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>hgb96throq5xc18yx132a5gtu2t1n51</sha1>
    </revision>
    <revision>
      <id>175</id>
      <parentid>174</parentid>
      <timestamp>2015-11-19T21:15:29Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5286">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which lines are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>tvp84yg39w38gxbxd0gb3hdgu293kv4</sha1>
    </revision>
    <revision>
      <id>176</id>
      <parentid>175</parentid>
      <timestamp>2015-11-20T18:28:59Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5288">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Load MatLab.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see a bunch of blurred circles with two stripes down the middle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>p753lsmtiv2o7woay25lnf2obyxg9j0</sha1>
    </revision>
    <revision>
      <id>178</id>
      <parentid>176</parentid>
      <timestamp>2015-11-30T22:22:19Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5340">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game.  In this game you are going to see patches of stripes arranged in a circle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor.
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>kivc852bps87iv2rnb0bxivgety2vfi</sha1>
    </revision>
    <revision>
      <id>180</id>
      <parentid>178</parentid>
      <timestamp>2015-12-01T23:17:51Z</timestamp>
      <contributor>
        <username>Alexwhite</username>
        <id>3</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5434">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game, and your goal is to win as many points as possible.  In this game you are going to see patches of stripes arranged in a circle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor. If you are correct, you win three points!
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
If you get it right, you will hear a short ding and see a green star in the middle of the screen over the +. This is what it looks like:
 [[File:FeedbackStar.jpg|800px]]
If you get it wrong, you won't hear anything, but you will see a red cross in the middle of the screen.
 [[File:RedCross.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>hay4p2v1wk33zsc0m81s7w4ul1ejzsj</sha1>
    </revision>
    <revision>
      <id>186</id>
      <parentid>180</parentid>
      <timestamp>2015-12-02T22:05:28Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Introduction */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5615">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game, and your goal is to win as many points as possible.  In this game you are going to see patches of stripes arranged in a circle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor. If you are correct, you win three points!
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
Because this is a game, you'll get points for correct responses and your goal is to see how many points you can get.  If you get over 700 points you'll get a prize to take home!
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:feedbackPointscorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:feedbackPointsError.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>24oobshpo1e1f9gieent1yvngwcwc6g</sha1>
    </revision>
    <revision>
      <id>187</id>
      <parentid>186</parentid>
      <timestamp>2015-12-02T22:09:15Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="5615">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot;

===Introduction===
In this task you are going to play a game, and your goal is to win as many points as possible.  In this game you are going to see patches of stripes arranged in a circle. This is what they look like:
 [[File:Gabors1.jpg|800px]]
Here's a close-up so you can see that they are intentionally blurry.  Do you notice anything about the stripes?
 [[File:GaborsCloseUp.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. This is important because the game is to find the stripes that are tilted to the side.  
As you can see all eight of the stripes are the same except for one.  For each task in the game you will need to find the ones that are tilted like this* or like this*
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
After finding the the tilted stripes, you will press either the Left Arrow key or the Right arrow key, depending on whether the tops of the stripes are pointing toward the Left or Right corner of the monitor. If you are correct, you win three points!
In the last image, you can see that the stripes are tilted so that they &quot;point&quot; at the upper right corner of the monitor.  In this case, you would press the Right Arrow key.
Here's one where you would press the Left Arrow key:
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]
Because this is a game, you'll get points for correct responses and your goal is to see how many points you can get.  If you get over 700 points you'll get a prize to take home!
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Let's try a practice round! 
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to give you a bit of help by showing a red dot next to the stripes that will be tilted.  Everything else is the same - you will still press either the Left or Right arrow based on which way the stripes are tilted - the only difference is that you won't have to figure out which one those are.  
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the cued version [1], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
 

===Introduce the Single Stimulus===
The final task is even easier.  Now there will only be one set of stripes on the screen, so you won't be distracted by having all 8.  Same rules apply for this one - all you need to do is figure out which way those blurry stripes are tilted.  
Here's an example:
 [[File:SingleStim3.jpg|800px]]
I think you've gotten the hang of it, but lets practice this one too.  This time we won't go for as long though.

===Practice Single Stimulus===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>95otnqlp9ujjtihzsrzbbwgticvtn1r</sha1>
    </revision>
    <revision>
      <id>205</id>
      <parentid>187</parentid>
      <timestamp>2015-12-10T23:39:55Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6552">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!


===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>316qz4h3baczst7qcnrdakdyx4gml50</sha1>
    </revision>
    <revision>
      <id>207</id>
      <parentid>205</parentid>
      <timestamp>2015-12-10T23:44:48Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6589">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!


===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>b1ebsjhqsy6bqgxj2is057902n8spqr</sha1>
    </revision>
    <revision>
      <id>215</id>
      <parentid>207</parentid>
      <timestamp>2016-01-08T20:42:47Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Attention: Spatial Cueing */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6588">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!


===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>fy0tljhav3qq1kdc316lp2v75u5yuym</sha1>
    </revision>
    <revision>
      <id>236</id>
      <parentid>215</parentid>
      <timestamp>2016-01-28T20:07:58Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* Block Series */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6772">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
The stripe patches show up quickly, but this is not a game of speed! You can take your time and there's no rush to respond.  Don't take too long though - you might forget what you saw!
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.</text>
      <sha1>jczec9p4iks6ye63housvy7znfxsmi9</sha1>
    </revision>
    <revision>
      <id>277</id>
      <parentid>236</parentid>
      <timestamp>2016-05-20T23:03:35Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6957">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
The stripe patches show up quickly, but this is not a game of speed! You can take your time and there's no rush to respond.  Don't take too long though - you might forget what you saw!
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.

===Version 3===
In version 3, there are now 8 blocks: 2 single stimulus, 2 uncued, 2 with a red cue, and 2 with a small black R&amp;H cue.
This is what the small black R&amp;H cue looks like:</text>
      <sha1>j4ab0s2dsrwm6u52grwbodutqay5cc8</sha1>
    </revision>
    <revision>
      <id>280</id>
      <parentid>277</parentid>
      <timestamp>2016-05-20T23:10:44Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6988">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
The stripe patches show up quickly, but this is not a game of speed! You can take your time and there's no rush to respond.  Don't take too long though - you might forget what you saw!
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.

===Version 3===
In version 3, there are now 8 blocks: 2 single stimulus, 2 uncued, 2 with a red cue, and 2 with a small black R&amp;H cue.
This is what the small black R&amp;H cue looks like:
 [[File:V3SmCueBot.jpg|800px]]</text>
      <sha1>5h7gx2n7rn8nxkjsf92minibstlbmrf</sha1>
    </revision>
    <revision>
      <id>281</id>
      <parentid>280</parentid>
      <timestamp>2016-05-20T23:12:59Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6988">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
The stripe patches show up quickly, but this is not a game of speed! You can take your time and there's no rush to respond.  Don't take too long though - you might forget what you saw!
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.

===Version 3===
In version 3, there are now 8 blocks: 2 single stimulus, 2 uncued, 2 with a red cue, and 2 with a small black R&amp;H cue.
This is what the small black R&amp;H cue looks like:
 [[File:V3SmCueBot.png|800px]]</text>
      <sha1>3f84bvpk9o2sw236v4jclebihm892mc</sha1>
    </revision>
    <revision>
      <id>283</id>
      <parentid>281</parentid>
      <timestamp>2016-06-06T22:47:10Z</timestamp>
      <contributor>
        <username>Alexwhite</username>
        <id>3</id>
      </contributor>
      <comment>/* Version 3 */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="6993">__TOC__
==Attention: Spatial Cueing==
 Spatial Cueing testing takes place in CHDD in Room 370
 # Ensure that Linux system is ready - with Chin Rest set-up
 # Perform Vision Test
 Open Terminal. Type &quot;sudo ptb3-matlab&quot;, then enter the password. 
 MatLab will load.  Navigate to the Code directory
   cd /.../code
 Ensure the script shows the correct monitor &quot;CHDD&quot; and the subject number is correct.

===Introduction===
 In this task you are going to play a game, and your goal is to win as many points as possible.  This game is a vision game and will test your powers of attention and detection.

===Introduce the Single Stimulus===
The first part of the game looks like this:
 [[File:SingleStim3.jpg|800px]]
The stripe patch is going to flash on the screen in one of eight spots around the screen and your job is to say which way the stripes are tilted by pressing the correct button on the keyboard. If they are tilted like this* towards this* corner of the screen, press the Left arrow.  
If they are tilted like this* towards the other corner of the screen, press the Right arrow instead.  
 [[File:singleSmallTilt2.jpg|800px]]
When you're playing the game, you're going to rest your chin here, and focus on the + in the middle of the screen. 
*During demonstration, use your hand to show the tilt of the stripes, pointing them toward the corners of the monitor, avoiding descriptions of Left or Right, or Clockwise or Counter-Clockwise
It doesn't matter where the patch of stripes is located on the screen, it only matters which way the stripes are tilted. 
If you get it right, you will hear a short ding and see a +3 in green at the center of the screen.  This is what it looks like:
 [[File:FeedbackPointsCorrect.jpg|800px]]
If you get it wrong, you will hear a different sound and +0 in the middle of the screen in red.
 [[File:FeedbackPointsError.jpg|800px]]  
Any questions? Ready to try a practice round of this part?

===Practice Single Stimulus===
 Follow embedded instructions, specifying that it is practice [y], the single stimulus version [2], the shorter version [n], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Uncued task===
In the last part, we make it a bit harder and get rid of that red dot.  In this task, your task will be to pay close attention to all of the stripes and find which one is tilted and press the correct button.  
In this one, it will be especially important to keep your eyes fixed on that +, so that you can see all of the patches, since you don't know which one is going to be different.
This is what they look like?
 [[File:GaborsCloseUp.jpg|800px]]
As you can see all eight of the stripes are the same except for one.  
 [[File:Gabors2.jpg|800px]]
It doesn't matter which side of the screen the tilted stripes are on, so even if they are on the right side of the screen, you would still press the Left Arrow since the tops of the stripes are pointing toward the Left Corner of the Monitor.  
It may seem easy now, but the tricky part of the task is that it gets harder and harder to tell which way the stripes are tilted, to the point that you have to pay extra close attention.  Which one is different in this picture?
 [[File:SmallTilt.jpg|800px]]

===Uncued Practice===
 Run CueingDL1.m
 Follow embedded instructions, specifying that it is practice [y], the uncued version [0], the long version [y], and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response
===Introduce the Cued task===
In the second task, we're going to make it a bit harder.  This time there will be 8 of those stripe patches, arranged in a circle and all of them will be the same except one.  In this game, we help you out and show a red dot next to the one that will be tilted.  
Your job will be to find that red dot, see the stripes that are tilted, and press the correct button.  It's important to note that in this game the red dot will ALWAYS be next to the stripes that are tilted and will appear just a split-second before the stripe patches.   
This is what it will look like:
 [[File:CuedGabor.jpg|800px]]
Or this:
 [[File:CuedGabor2.jpg|800px]]
Let's practice this version!
Remember to keep you chin on the rest, and focus on that +!

===Cued Practice===
 Continue embedded instructions, specifying that it is another practice [y], the cued version [1], the long/short version, and initialize the tilt level in degrees [ex. 30]
 NOTE: If you need to exit the task at any time, press the 'Q' key instead of a Left/Right Arrow response

===Additional Practice===
This is a subjective decision following the above practice to add additional practice rounds based on how the subject is feeling and performing on each stimulus type.  
Regardless, at minimum, the subject should perform the uncued task with an initial tilt level at 15 or below.  
Run a few iterations of the practice rounds, at short duration.

===Block Series===
Now that you're done with the practice and proven yourself an expert, lets put those skills to the test.  In the real game, you will play the different tasks in this order: single path, uncued, cued, uncued, cued, single patch.  Each game is going to have more of each task and we're going to add a feature to make it a bit harder. 
This game is designed so that every time you get the right answer, the next one is going to be a bit harder by making the tilt harder and harder to see*.
*demonstrate with your hand that the tilt angle will get progressively shallower.
The game works the opposite way too - if you get one wrong, the next one is going to be a bit easier. 
You might have noticed that when you were doing the practice every time you got a correct answer you saw a +3 in the middle of the screen, and every time you got it wrong you got a +0.  In the practice the points didn't matter, but now that we're starting the real game, the challenge is to see how many points you can get!
To win the game, you need to get more than 700 points! 
The stripe patches show up quickly, but this is not a game of speed! You can take your time and there's no rush to respond.  Don't take too long though - you might forget what you saw!
&lt;br&gt;
Remember, keep your chin on the rest and focus hard on that + in the middle of the screen to give your eyes the best chance of detecting which stripes are not like the others!

===Run the Series===
 Determine in script the number of blocks.
 Run CueingDL1.m
 Follow embedded instruction, specifying that it is NOT practice [n] and set the threshold level at a level based on the performance during practice - somewhere in the range of 15-20 should be ideal.

===Version 3===
In version 3, there are now 8 blocks: 2 single stimulus, 2 uncued, 2 with a red cue, and 2 with a small black R&amp;H cue.
This is what the small black R&amp;H cue looks like:
 [[File:V3SmCueBotCrop.png |800px]]</text>
      <sha1>5je389117y8zarp2yloqzc1fsep1su0</sha1>
    </revision>
  </page>
  <page>
    <title>Reading Instruction</title>
    <ns>0</ns>
    <id>31</id>
    <revision>
      <id>182</id>
      <timestamp>2015-12-02T00:31:26Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;__TOC__ ==What Works Clearinghous== A great resource evaluating scientific evidence for various education programs. http://ies.ed.gov/ncee/wwc/default.aspx&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="155">__TOC__
==What Works Clearinghous==
A great resource evaluating scientific evidence for various education programs.
http://ies.ed.gov/ncee/wwc/default.aspx</text>
      <sha1>s2pi755w3008w9b61mpqkdtg970r04i</sha1>
    </revision>
    <revision>
      <id>188</id>
      <parentid>182</parentid>
      <timestamp>2015-12-02T22:33:01Z</timestamp>
      <contributor>
        <username>Pdonnelly</username>
        <id>2</id>
      </contributor>
      <comment>/* What Works Clearinghous */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="156">__TOC__
==What Works Clearinghouse==
A great resource evaluating scientific evidence for various education programs.
http://ies.ed.gov/ncee/wwc/default.aspx</text>
      <sha1>qnu9x8jhssntvu32zqf8vbuv9r95e0o</sha1>
    </revision>
  </page>
  <page>
    <title>Software Setup</title>
    <ns>0</ns>
    <id>17</id>
    <revision>
      <id>124</id>
      <timestamp>2015-11-03T01:29:31Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>Created page with &quot;We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on githu...&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="880">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code. e.g.
 git clone https://github.com/yeatmanlab/mritools.git
==Yeatman Lab Tools==
 https://github.com/yeatmanlab/mritools
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 https://github.com/vistalab/vistasoft
==Automated Fiber Quantification==
 https://github.com/jyeatman/AFQ
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using apt-get:
 apt-get update
 apt-get install python-nibabel
or pip
  pip install nibabel</text>
      <sha1>pvp6z30cd4dl1skzaq6j0lqjlympcaw</sha1>
    </revision>
    <revision>
      <id>126</id>
      <parentid>124</parentid>
      <timestamp>2015-11-03T01:30:51Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="879">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code. e.g.
 git clone https://github.com/yeatmanlab/mritools.git
==Yeatman Lab Tools==
 https://github.com/yeatmanlab/mritools
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 https://github.com/vistalab/vistasoft
==Automated Fiber Quantification==
 https://github.com/jyeatman/AFQ
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using apt-get:
 apt-get update
 apt-get install python-nibabel
or pip
 pip install nibabel</text>
      <sha1>cgzi8hynnlnaqyxyo35b974pe20hs8e</sha1>
    </revision>
    <revision>
      <id>127</id>
      <parentid>126</parentid>
      <timestamp>2015-11-03T20:07:50Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* nibabel */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="820">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code. e.g.
 git clone https://github.com/yeatmanlab/mritools.git
==Yeatman Lab Tools==
 https://github.com/yeatmanlab/mritools
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 https://github.com/vistalab/vistasoft
==Automated Fiber Quantification==
 https://github.com/jyeatman/AFQ
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel</text>
      <sha1>1g7tiupooxycunic7t4rd35jmtx6i9a</sha1>
    </revision>
    <revision>
      <id>128</id>
      <parentid>127</parentid>
      <timestamp>2015-11-03T20:09:11Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="963">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code. e.g.
 git clone https://github.com/yeatmanlab/mritools.git
==Yeatman Lab Tools==
 https://github.com/yeatmanlab/mritools
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 https://github.com/vistalab/vistasoft
==Automated Fiber Quantification==
 https://github.com/jyeatman/AFQ
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel</text>
      <sha1>9pvqj4klg904vyunf4bmt30luejcoyv</sha1>
    </revision>
    <revision>
      <id>212</id>
      <parentid>128</parentid>
      <timestamp>2015-12-17T22:56:36Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1007">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code. e.g.
 git clone https://github.com/yeatmanlab/mritools.git
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel</text>
      <sha1>htkxs63s37wutd30wzq8z3gwflm4quk</sha1>
    </revision>
    <revision>
      <id>251</id>
      <parentid>212</parentid>
      <timestamp>2016-03-02T22:36:17Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1169">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9</text>
      <sha1>94xxzpuc8qccadg1co4qdu46xr008rf</sha1>
    </revision>
    <revision>
      <id>252</id>
      <parentid>251</parentid>
      <timestamp>2016-03-02T22:41:21Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Neurodebian repo */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1322">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update</text>
      <sha1>o8tsadc3e2f6srqpzf66vgwvh8le03p</sha1>
    </revision>
    <revision>
      <id>253</id>
      <parentid>252</parentid>
      <timestamp>2016-03-02T22:42:20Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1456">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update
==FSL==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get instal fsl-5.0-complete</text>
      <sha1>d8ws2a0vz2voauvn7je3tyurybuwcgs</sha1>
    </revision>
    <revision>
      <id>254</id>
      <parentid>253</parentid>
      <timestamp>2016-03-02T22:47:47Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* FSL */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1520">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update
==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get instal fsl-5.0-complete
 sudo apt-get install ants</text>
      <sha1>ev502oin49opd1mozwok8p1v5s42trp</sha1>
    </revision>
    <revision>
      <id>255</id>
      <parentid>254</parentid>
      <timestamp>2016-03-07T19:56:20Z</timestamp>
      <contributor>
        <username>Ehuber</username>
        <id>4</id>
      </contributor>
      <minor/>
      <comment>/* FSL, ANTS and other Neurodebian packages */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1521">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update
==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install ants</text>
      <sha1>7c68zbzajsm55oc3d034ge9ttoowm43</sha1>
    </revision>
    <revision>
      <id>256</id>
      <parentid>255</parentid>
      <timestamp>2016-03-09T00:20:48Z</timestamp>
      <contributor>
        <username>Ehuber</username>
        <id>4</id>
      </contributor>
      <minor/>
      <comment>/* FSL, ANTS and other Neurodebian packages */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1564">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update
==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants</text>
      <sha1>miwsoj63a8vdpvnouczcjjyuifx28bw</sha1>
    </revision>
    <revision>
      <id>257</id>
      <parentid>256</parentid>
      <timestamp>2016-03-21T16:14:56Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1615">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update
==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants</text>
      <sha1>6eesk5cbtmq59upu7up7q5lswtep4kh</sha1>
    </revision>
    <revision>
      <id>258</id>
      <parentid>257</parentid>
      <timestamp>2016-03-21T22:53:58Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1642">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The neurodebian project is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update
==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants</text>
      <sha1>6vlumdmt0944j6ila11i0b1vtzyjxu8</sha1>
    </revision>
    <revision>
      <id>259</id>
      <parentid>258</parentid>
      <timestamp>2016-03-21T23:47:42Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Neurodebian repo */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1670">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants</text>
      <sha1>4e3lbpqzbts927u668vkgl6u7udcbr5</sha1>
    </revision>
    <revision>
      <id>260</id>
      <parentid>259</parentid>
      <timestamp>2016-03-21T23:58:08Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>Added psychtoolbox install</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1781">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3</text>
      <sha1>gdw3um44kue89gv886fncsfisb8i61f</sha1>
    </revision>
    <revision>
      <id>261</id>
      <parentid>260</parentid>
      <timestamp>2016-03-23T23:22:23Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>added install instructions for FSL</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1917">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. See instructions here:
 https://www.continuum.io/downloads
==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit .bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3</text>
      <sha1>098jx59gt1z01w2ttnmlf86mjd52gg8</sha1>
    </revision>
    <revision>
      <id>284</id>
      <parentid>261</parentid>
      <timestamp>2016-06-08T20:05:55Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Anaconda */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2230">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit .bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3</text>
      <sha1>godc7m406syv8b4y357gpr664kt3gbb</sha1>
    </revision>
    <revision>
      <id>285</id>
      <parentid>284</parentid>
      <timestamp>2016-06-10T17:08:30Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* FSL, ANTS and other Neurodebian packages */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2232">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3</text>
      <sha1>d68dhr4pbddxvqsf2vtws9itf2i6dvh</sha1>
    </revision>
    <revision>
      <id>286</id>
      <parentid>285</parentid>
      <timestamp>2016-06-10T17:15:31Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* FSL, ANTS and other Neurodebian packages */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2237">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 sudo gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3</text>
      <sha1>lc44wkg527nk6h08dlps8hveci2ifa5</sha1>
    </revision>
    <revision>
      <id>287</id>
      <parentid>286</parentid>
      <timestamp>2016-06-10T17:36:21Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* FSL, ANTS and other Neurodebian packages */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2232">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3</text>
      <sha1>d68dhr4pbddxvqsf2vtws9itf2i6dvh</sha1>
    </revision>
    <revision>
      <id>288</id>
      <parentid>287</parentid>
      <timestamp>2016-06-10T17:39:10Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Psychtoolbox for Matlab */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2311">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
 sudo adduser userid
==Yeatman Lab Tools==
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 git clone https://github.com/jyeatman/AFQ.git
==Anaconda==
Each user should have anaconda set up to manage their Python packages. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>fwd0yvtd5jbl8pbpjbb3jiccdj4fu49</sha1>
    </revision>
    <revision>
      <id>289</id>
      <parentid>288</parentid>
      <timestamp>2016-06-11T00:35:49Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="3726">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges:
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
 addpath(genpath('~/Documents/MATLAB/spm8/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>1p9emonv4d39otxpb191uqg0qov37jz</sha1>
    </revision>
    <revision>
      <id>290</id>
      <parentid>289</parentid>
      <timestamp>2016-06-13T16:07:57Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Setting up a user account */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="3782">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
 addpath(genpath('~/Documents/MATLAB/spm8/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy
==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>sps6zxn4y7ghituqcgpd7vtm3thqm55</sha1>
    </revision>
    <revision>
      <id>295</id>
      <parentid>290</parentid>
      <timestamp>2016-06-14T20:37:55Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4027">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
 addpath(genpath('~/Documents/MATLAB/spm8/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>1oox419r7auyc2m8pnpna3jd0gtziam</sha1>
    </revision>
    <revision>
      <id>296</id>
      <parentid>295</parentid>
      <timestamp>2016-06-14T20:43:08Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* pyAFQ */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4203">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
 addpath(genpath('~/Documents/MATLAB/spm8/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 gedit ~/.bashrc
Copy the following code (replacing userid) into the file that opens and save.
 # Add local bin directory to path
 export PATH=&quot;/home/userid/.local/bin/:$PATH&quot;

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>rtsns9s655plgyu6n679db3ftblvjtp</sha1>
    </revision>
    <revision>
      <id>297</id>
      <parentid>296</parentid>
      <timestamp>2016-06-14T21:23:49Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* pyAFQ */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4222">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
 addpath(genpath('~/Documents/MATLAB/spm8/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3
 gedit ~/.bashrc
Copy the following code (replacing userid) into the file that opens and save.
 # Add local bin directory to path
 export PATH=&quot;/home/userid/.local/bin/:$PATH&quot;

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>7lrwit6kuye0foi31dfffqgy6txmji5</sha1>
    </revision>
    <revision>
      <id>305</id>
      <parentid>297</parentid>
      <timestamp>2016-07-05T16:10:58Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Setting up Matlab for AFQ */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4222">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/Documents/MATLAB/spm8/'))
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3
 gedit ~/.bashrc
Copy the following code (replacing userid) into the file that opens and save.
 # Add local bin directory to path
 export PATH=&quot;/home/userid/.local/bin/:$PATH&quot;

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>a97ekh65uz304upb7se4di8agmo9bvg</sha1>
    </revision>
    <revision>
      <id>306</id>
      <parentid>305</parentid>
      <timestamp>2016-07-06T18:48:53Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* pyAFQ */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4046">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, do not create a /usr/local/bin file during installation. Instead, after installing r2014a to the default location.
 cd /usr/local/bin
 sudo ln -s /usr/local/MATLAB/R2014a/bin/matlab AFQ-matlab
 sudo AFQ-matlab
In the Matlab terminal that opens,
 cd /usr/local/MATLAB/R2014a/toolbox/local
 edit startup
Paste the following code into the new file.
 addpath(genpath('~/Documents/MATLAB/spm8/'))
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))
Save the file

If all the requisite dependencies for AFQ have been installed according the instructions on this wiki, you can now execute
 AFQ-matlab
from the terminal in order to launch Matlab r2014a with all the necessary dependencies.

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>toibmsx652ipghlq7jeoqwdscl4ptm6</sha1>
    </revision>
    <revision>
      <id>307</id>
      <parentid>306</parentid>
      <timestamp>2016-07-13T17:59:21Z</timestamp>
      <contributor>
        <username>Jyeatman</username>
        <id>1</id>
      </contributor>
      <comment>/* Setting up Matlab for AFQ */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="3822">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, you can set aliases in your ~/.bashrc file
 alias matlabr2016=&quot;/usr/local/MATLAB/R2016a/bin/matlab&quot;
For Matlab to see all of the necessary code create a file named startup.m and place it in your home directory. The startup file gets executed automatically when Matlab starts up. To add folders of code to your Matlab search path (making it visible to Matlab):
 addpath(genpath('~/Documents/MATLAB/spm8/'))
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>8og5dzgxqycfo2w2b9b932rtjctfbz6</sha1>
    </revision>
    <revision>
      <id>308</id>
      <parentid>307</parentid>
      <timestamp>2016-07-13T18:16:55Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4716">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, you can set aliases in your ~/.bashrc file
 alias matlabr2016=&quot;/usr/local/MATLAB/R2016a/bin/matlab&quot;
For Matlab to see all of the necessary code create a file named startup.m and place it in your home directory. The startup file gets executed automatically when Matlab starts up. To add folders of code to your Matlab search path (making it visible to Matlab):
 addpath(genpath('~/Documents/MATLAB/spm8/'))
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Freesurfer==
The steps and download is [https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall here], instructions are repeated below. Download the Linux CentOS 6 x86_64 install file.
 cd ~/Downloads
 sudo tar -C /usr/local -xzvf freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0.tar.gz
You'll need to register [https://surfer.nmr.mgh.harvard.edu/registration.html here]. The registration e-mail will tell you which text to copy into a new file, which you can create/edit by
 sudo gedit /usr/local/freesurfer/license.txt
We'll be creating a directory for processed subject files on the scratch drive.
 mkdir /mnt/scratch/projects/freesurfer
Finally, you'll need to edit your bashrc file.
 gedit ~/.bashrc
Copy the following text
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/scratch/projects/freesurfer

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>nachz2zg6t94zz63w5vqcnca76sxkbu</sha1>
    </revision>
    <revision>
      <id>309</id>
      <parentid>308</parentid>
      <timestamp>2016-07-13T18:20:33Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Freesurfer */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4719">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, you can set aliases in your ~/.bashrc file
 alias matlabr2016=&quot;/usr/local/MATLAB/R2016a/bin/matlab&quot;
For Matlab to see all of the necessary code create a file named startup.m and place it in your home directory. The startup file gets executed automatically when Matlab starts up. To add folders of code to your Matlab search path (making it visible to Matlab):
 addpath(genpath('~/Documents/MATLAB/spm8/'))
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Freesurfer==
The steps and download is [https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall here], instructions are repeated below. Download the Linux CentOS 6 x86_64 install file.
 cd ~/Downloads
 sudo tar -C /usr/local -xzvf freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0.tar.gz
You'll need to register [https://surfer.nmr.mgh.harvard.edu/registration.html here]. The registration e-mail will tell you which text to copy into a new file, which you can create/edit by
 sudo gedit /usr/local/freesurfer/license.txt
We'll be creating a directory for processed subject files on the scratch drive.
 mkdir -p /mnt/scratch/projects/freesurfer
Finally, you'll need to edit your bashrc file.
 gedit ~/.bashrc
Copy the following text
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/scratch/projects/freesurfer

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>jfkxdgfwkhtm6icig4m8r4h6xhgtu25</sha1>
    </revision>
    <revision>
      <id>310</id>
      <parentid>309</parentid>
      <timestamp>2016-07-13T19:08:04Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Freesurfer */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="4865">We rely on a number of different software packages to analyze MRI and MEG data. Here is a growing tally of what we use and where to get it. Most of these tools reside on github and you should definitely use git to clone the repositories rather than download a snapshot of the code.
==Setting up a user account==
To add a new user with sudo privileges (userid should be maintained across all systems).
 sudo useradd -m -G bde,sudo -s /bin/bash userid
To set user password.
 sudo passwd userid
Have new user enter desired password.

==Git==
All git directories should be maintained together.
 mkdir ~/git
==Yeatman Lab Tools==
 cd ~/git
 git clone https://github.com/yeatmanlab/BrainTools.git
==Vistasoft==
MATLAB based toolbox, from Brian Wandell's lab at Stanford, that contains many functions we rely on for analyzing diffusion MRI and functional MRI data
 cd ~/git
 git clone https://github.com/vistalab/vistasoft.git
==Automated Fiber Quantification==
 cd ~/git
 git clone https://github.com/jyeatman/AFQ.git

==Setting up Matlab for AFQ==
AFQ requires Matlab r2014a, SPM8, Vistasoft, and AFQ. If you wish to use a different version of Matlab for your core coding, you can set aliases in your ~/.bashrc file
 alias matlabr2016=&quot;/usr/local/MATLAB/R2016a/bin/matlab&quot;
For Matlab to see all of the necessary code create a file named startup.m and place it in your home directory. The startup file gets executed automatically when Matlab starts up. To add folders of code to your Matlab search path (making it visible to Matlab):
 addpath(genpath('~/Documents/MATLAB/spm8/'))
 addpath(genpath('~/git/AFQ/'))
 addpath(genpath('~/git/BrainTools/'))
 addpath(genpath('~/git/vistasoft/'))

==Anaconda==
Each user should have anaconda set up to manage their Python packages. Do this before installing nibabel and dipy. Anaconda will manage versions and dependencies for each user separately. Each install will be specific to the user that installs it. Do not install as root, as you will deny yourself privileges to your own directory. You must restart your terminal session after install before using Python or the conda command. See instructions here:
 https://www.continuum.io/downloads

==nibabel and DIPY==
Python based toolbox for dealing with nifti images. While nibabel is on github we suggest installing using pip:
 pip install nibabel
 pip install dipy

==pyAFQ==
While we are still in the processing of developing a full implementation of AFQ in Python, some of these files are required for running DKI.
 cd ~/git
 git clone https://github.com/yeatmanlab/pyAFQ
 cd pyAFQ
 python setup.py develop
 pip install boto3

==Neurodebian repo==
The [http://neuro.debian.net/ neurodebian project] is an incredible resource making it easy to install most of the widely used nueroimaging software packages.
 wget -O- http://neuro.debian.net/lists/trusty.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
 sudo apt-key adv --recv-keys --keyserver hkp://pgp.mit.edu:80 0xA5D32F012649A5A9
 sudo apt-get update

==FSL, ANTS and other Neurodebian packages==
Once you have added the neurodebian repos you can easily install fsl and other packages
 sudo apt-get install fsl-5.0-complete
 sudo apt-get install fsl-5.0-eddy-nonfree
 sudo apt-get install ants

FSL requires an edit to the .bashrc file
 gedit ~/.bashrc
Copy the following into the file that open
 # FSL setup
 . /etc/fsl/5.0/fsl.sh

==Freesurfer==
The steps and download is [https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall here], instructions are repeated below. Download the Linux CentOS 6 x86_64 install file.
 cd ~/Downloads
 sudo tar -C /usr/local -xzvf freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0.tar.gz
You'll need to register [https://surfer.nmr.mgh.harvard.edu/registration.html here]. The registration e-mail will tell you which text to copy into a new file, which you can create/edit by
 sudo gedit /usr/local/freesurfer/license.txt
We'll be creating a directory for processed subject files on the scratch drive.
 mkdir -p /mnt/scratch/projects/freesurfer
We need to resolve an issue with a jpeg library by creating a symbolic link.
 cd /usr/lib/x86_64-linux-gnu
 sudo ln -s libjpeg.so.8 libjpeg.so.62
Finally, you'll need to edit your bashrc file.
 gedit ~/.bashrc
Copy the following text
 export FREESURFER_HOME=/usr/local/freesurfer
 source $FREESURFER_HOME/SetUpFreeSurfer.sh &gt; /dev/null
 export SUBJECTS_DIR=/mnt/scratch/projects/freesurfer

==SPM8==
Fill out the form located [http://www.fil.ion.ucl.ac.uk/spm/software/download/ here] in order, making sure to select SPM8. Run this script from the terminal to install.
 unzip ~/Downloads/spm8.zip -d ~/Documents/MATLAB/


==Psychtoolbox for Matlab==
Requires Neurodebian repo to install.
 sudo apt-get install matlab-psychtoolbox-3
In order to launch Matlab with Psychtoolbox, use terminal command
 ptb3-matlab</text>
      <sha1>lpzd5a5zclfusejwcspbuje0axycmg4</sha1>
    </revision>
  </page>
  <page>
    <title>System Setup</title>
    <ns>0</ns>
    <id>51</id>
    <revision>
      <id>293</id>
      <timestamp>2016-06-13T19:56:52Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1859">This page will eventually have a detailed description for a clean install/upgrade of the lab OS. For now, helpful tips will be posted here to make sure somewhat complicated tasks are done correctly. In all examples
 userid
should be replaced with the currently active userid (your login).

==Mount Drives==
If you have installed a new hard drive or have upgraded or changed your OS, you may need to remount your scratch drives and give yourself permissions to access them. Before beginning, you should make sure that you have sudo privileges and membership in the bde group. To check this, in the terminal, type
 groups userid
If you are not a member of the group sudo, you will need to request sudo privileges from another member of the lab. If you are not a member of bde,
 sudo groupadd bde
 sudo usermod -G bde
Now, to find the UUID of the drive you are mounting:
 sudo blkid
The desired drive should be listed as /dev/sbd (with a number after it if you have several additional drives installed).
Edit the fstab file to specify mount instructions.
 sudo gedit /etc/fstab
Copy the following code, replacing the example UUID with that specific to your machine:
 # Mount for scratch space
 UUID=7fx5ebx6-b1x2-47ce-882b-af780899189a /mnt/scratch    ext4    defaults	  0	  0
Save and exit the file.
To mount,
 sudo mount -a
If you get an error telling you scratch does not exist,
 sudo mkdir /mnt/scratch
Now we set permissions:
 FINISH EDITING THIS SECTION

==Printer Setup==
Our mighty printer, [https://www.youtube.com/watch?v=m96VcCF4Ess Elwha], never jams, and prints duplex and in color.
 ping elwha.ilabs.uw.edu
Note the IP address listed.
 sudo system-config-printer
Click add, expand the selection for network printer, and find the printer tied to the IP address you got from pinging Elwha (HP Color LaserJet MFP M477fdn). Use the recommended drivers.</text>
      <sha1>ib5y7udd7g77k81ev0h89hmsq9untua</sha1>
    </revision>
    <revision>
      <id>294</id>
      <parentid>293</parentid>
      <timestamp>2016-06-13T20:31:52Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="1869">This page will eventually have a detailed description for a clean install/upgrade of the lab OS. For now, helpful tips will be posted here to make sure somewhat complicated tasks are done correctly. In all examples
 userid
should be replaced with the currently active userid (your login).

==Mount Drives==
If you have installed a new hard drive or have upgraded or changed your OS, you may need to remount your scratch drives and give yourself permissions to access them. Before beginning, you should make sure that you have sudo privileges and membership in the bde group. To check this, in the terminal, type
 groups userid
If you are not a member of the group sudo, you will need to request sudo privileges from another member of the lab. If you are not a member of bde,
 sudo groupadd bde
 sudo usermod -G bde
Now, to find the UUID of the drive you are mounting:
 sudo blkid
The desired drive should be listed as /dev/sbd (with a number after it if you have several additional drives installed).
Edit the fstab file to specify mount instructions.
 sudo gedit /etc/fstab
Copy the following code, replacing the example UUID with that specific to your machine:
 # Mount for scratch space
 UUID=7fx5ebx6-b1x2-47ce-882b-af780899189a /mnt/scratch    ext4    defaults	  0	  0
Save and exit the file.
To mount,
 sudo mount -a
If you get an error telling you scratch does not exist,
 sudo mkdir /mnt/scratch
Now we set permissions:
 sudo chown -R userid:bde /mnt/scratch

==Printer Setup==
Our mighty printer, [https://www.youtube.com/watch?v=m96VcCF4Ess Elwha], never jams, and prints duplex and in color.
 ping elwha.ilabs.uw.edu
Note the IP address listed.
 sudo system-config-printer
Click add, expand the selection for network printer, and find the printer tied to the IP address you got from pinging Elwha (HP Color LaserJet MFP M477fdn). Use the recommended drivers.</text>
      <sha1>j1xxb0xn6iqkvhpe3hkr4upy2woadam</sha1>
    </revision>
    <revision>
      <id>298</id>
      <parentid>294</parentid>
      <timestamp>2016-06-14T23:11:08Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2406">This page will eventually have a detailed description for a clean install/upgrade of the lab OS. For now, helpful tips will be posted here to make sure somewhat complicated tasks are done correctly. In all examples
 userid
should be replaced with the currently active userid (your login).

==Static IP Address==
You will automatically pull an IP address from the network when you plug in, but in order to be able to ssh into your machine remotely and communicate on the cluster, you will need a static IP address set. This can be done through the network connections setting in the GUI (lower right in Mint). Click network connections, select your currently active connection, click the IPv4 Settings tab, select 'Manual' from the drop down Method: list. Fill out the information found under 'Networking' in the lab handbook.

==Mount Drives==
If you have installed a new hard drive or have upgraded or changed your OS, you may need to remount your scratch drives and give yourself permissions to access them. Before beginning, you should make sure that you have sudo privileges and membership in the bde group. To check this, in the terminal, type
 groups userid
If you are not a member of the group sudo, you will need to request sudo privileges from another member of the lab. If you are not a member of bde,
 sudo groupadd bde
 sudo usermod -G bde
Now, to find the UUID of the drive you are mounting:
 sudo blkid
The desired drive should be listed as /dev/sbd (with a number after it if you have several additional drives installed).
Edit the fstab file to specify mount instructions.
 sudo gedit /etc/fstab
Copy the following code, replacing the example UUID with that specific to your machine:
 # Mount for scratch space
 UUID=7fx5ebx6-b1x2-47ce-882b-af780899189a /mnt/scratch    ext4    defaults	  0	  0
Save and exit the file.
To mount,
 sudo mount -a
If you get an error telling you scratch does not exist,
 sudo mkdir /mnt/scratch
Now we set permissions:
 sudo chown -R userid:bde /mnt/scratch

==Printer Setup==
Our mighty printer, [https://www.youtube.com/watch?v=m96VcCF4Ess Elwha], never jams, and prints duplex and in color.
 ping elwha.ilabs.uw.edu
Note the IP address listed.
 sudo system-config-printer
Click add, expand the selection for network printer, and find the printer tied to the IP address you got from pinging Elwha (HP Color LaserJet MFP M477fdn). Use the recommended drivers.</text>
      <sha1>8s9kkggtirl6rfnq8zlx66crxwhzatg</sha1>
    </revision>
    <revision>
      <id>301</id>
      <parentid>298</parentid>
      <timestamp>2016-06-20T21:05:28Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2730">This page will eventually have a detailed description for a clean install/upgrade of the lab OS. For now, helpful tips will be posted here to make sure somewhat complicated tasks are done correctly. In all examples
 userid
should be replaced with the currently active userid (your login).

==Static IP Address==
You will automatically pull an IP address from the network when you plug in, but in order to be able to ssh into your machine remotely and communicate on the cluster, you will need a static IP address set. This can be done through the network connections setting in the GUI (lower right in Mint). Click network connections, select your currently active connection, click the IPv4 Settings tab, select 'Manual' from the drop down Method: list. Fill out the information found under 'Networking' in the lab handbook.

==New Hard Drive==
If you installed a new hard drive on your machine, first find out where it is located. Take note of the logical name printed out by the following:
 sudo lshw -C disk
This should be /dev/sd'''x''' (replace this x with the letter printed). To format the drive as one partition,
 sudo mkfs -t ext4 /dev/sdx

==Mount Drives==
If you have installed a new hard drive or have upgraded or changed your OS, you may need to remount your scratch drives and give yourself permissions to access them. Before beginning, you should make sure that you have sudo privileges and membership in the bde group. To check this, in the terminal, type
 groups userid
If you are not a member of the group sudo, you will need to request sudo privileges from another member of the lab. If you are not a member of bde,
 sudo groupadd bde
 sudo usermod -G bde
Now, to find the UUID of the drive you are mounting:
 sudo blkid
The desired drive should be listed as /dev/sbd (with a number after it if you have several additional drives installed).
Edit the fstab file to specify mount instructions.
 sudo gedit /etc/fstab
Copy the following code, replacing the example UUID with that specific to your machine:
 # Mount for scratch space
 UUID=7fx5ebx6-b1x2-47ce-882b-af780899189a /mnt/scratch    ext4    defaults	  0	  0
Save and exit the file.
To mount,
 sudo mount -a
If you get an error telling you scratch does not exist,
 sudo mkdir /mnt/scratch
Now we set permissions:
 sudo chown -R userid:bde /mnt/scratch

==Printer Setup==
Our mighty printer, [https://www.youtube.com/watch?v=m96VcCF4Ess Elwha], never jams, and prints duplex and in color.
 ping elwha.ilabs.uw.edu
Note the IP address listed.
 sudo system-config-printer
Click add, expand the selection for network printer, and find the printer tied to the IP address you got from pinging Elwha (HP Color LaserJet MFP M477fdn). Use the recommended drivers.</text>
      <sha1>ib68mf6qlk0raebe8t8wb048h9gyp6v</sha1>
    </revision>
    <revision>
      <id>302</id>
      <parentid>301</parentid>
      <timestamp>2016-06-20T21:05:53Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* New Hard Drive */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2736">This page will eventually have a detailed description for a clean install/upgrade of the lab OS. For now, helpful tips will be posted here to make sure somewhat complicated tasks are done correctly. In all examples
 userid
should be replaced with the currently active userid (your login).

==Static IP Address==
You will automatically pull an IP address from the network when you plug in, but in order to be able to ssh into your machine remotely and communicate on the cluster, you will need a static IP address set. This can be done through the network connections setting in the GUI (lower right in Mint). Click network connections, select your currently active connection, click the IPv4 Settings tab, select 'Manual' from the drop down Method: list. Fill out the information found under 'Networking' in the lab handbook.

==New Hard Drive==
If you installed a new hard drive on your machine, first find out where it is located. Take note of the logical name printed out by the following:
 sudo lshw -C disk
This should be /dev/sd'''x''' (replace this x with the letter printed). To format the drive as one partition,
 sudo mkfs -t ext4 /dev/sd'''x'''

==Mount Drives==
If you have installed a new hard drive or have upgraded or changed your OS, you may need to remount your scratch drives and give yourself permissions to access them. Before beginning, you should make sure that you have sudo privileges and membership in the bde group. To check this, in the terminal, type
 groups userid
If you are not a member of the group sudo, you will need to request sudo privileges from another member of the lab. If you are not a member of bde,
 sudo groupadd bde
 sudo usermod -G bde
Now, to find the UUID of the drive you are mounting:
 sudo blkid
The desired drive should be listed as /dev/sbd (with a number after it if you have several additional drives installed).
Edit the fstab file to specify mount instructions.
 sudo gedit /etc/fstab
Copy the following code, replacing the example UUID with that specific to your machine:
 # Mount for scratch space
 UUID=7fx5ebx6-b1x2-47ce-882b-af780899189a /mnt/scratch    ext4    defaults	  0	  0
Save and exit the file.
To mount,
 sudo mount -a
If you get an error telling you scratch does not exist,
 sudo mkdir /mnt/scratch
Now we set permissions:
 sudo chown -R userid:bde /mnt/scratch

==Printer Setup==
Our mighty printer, [https://www.youtube.com/watch?v=m96VcCF4Ess Elwha], never jams, and prints duplex and in color.
 ping elwha.ilabs.uw.edu
Note the IP address listed.
 sudo system-config-printer
Click add, expand the selection for network printer, and find the printer tied to the IP address you got from pinging Elwha (HP Color LaserJet MFP M477fdn). Use the recommended drivers.</text>
      <sha1>kiyli520keqkaksshybn04dijlq4m6v</sha1>
    </revision>
    <revision>
      <id>311</id>
      <parentid>302</parentid>
      <timestamp>2016-07-27T17:24:53Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>/* Mount Drives */</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="2739">This page will eventually have a detailed description for a clean install/upgrade of the lab OS. For now, helpful tips will be posted here to make sure somewhat complicated tasks are done correctly. In all examples
 userid
should be replaced with the currently active userid (your login).

==Static IP Address==
You will automatically pull an IP address from the network when you plug in, but in order to be able to ssh into your machine remotely and communicate on the cluster, you will need a static IP address set. This can be done through the network connections setting in the GUI (lower right in Mint). Click network connections, select your currently active connection, click the IPv4 Settings tab, select 'Manual' from the drop down Method: list. Fill out the information found under 'Networking' in the lab handbook.

==New Hard Drive==
If you installed a new hard drive on your machine, first find out where it is located. Take note of the logical name printed out by the following:
 sudo lshw -C disk
This should be /dev/sd'''x''' (replace this x with the letter printed). To format the drive as one partition,
 sudo mkfs -t ext4 /dev/sd'''x'''

==Mount Drives==
If you have installed a new hard drive or have upgraded or changed your OS, you may need to remount your scratch drives and give yourself permissions to access them. Before beginning, you should make sure that you have sudo privileges and membership in the bde group. To check this, in the terminal, type
 groups userid
If you are not a member of the group sudo, you will need to request sudo privileges from another member of the lab. If you are not a member of bde,
 sudo groupadd bde
 sudo usermod -G -a bde
Now, to find the UUID of the drive you are mounting:
 sudo blkid
The desired drive should be listed as /dev/sbd (with a number after it if you have several additional drives installed).
Edit the fstab file to specify mount instructions.
 sudo gedit /etc/fstab
Copy the following code, replacing the example UUID with that specific to your machine:
 # Mount for scratch space
 UUID=7fx5ebx6-b1x2-47ce-882b-af780899189a /mnt/scratch    ext4    defaults	  0	  0
Save and exit the file.
To mount,
 sudo mount -a
If you get an error telling you scratch does not exist,
 sudo mkdir /mnt/scratch
Now we set permissions:
 sudo chown -R userid:bde /mnt/scratch

==Printer Setup==
Our mighty printer, [https://www.youtube.com/watch?v=m96VcCF4Ess Elwha], never jams, and prints duplex and in color.
 ping elwha.ilabs.uw.edu
Note the IP address listed.
 sudo system-config-printer
Click add, expand the selection for network printer, and find the printer tied to the IP address you got from pinging Elwha (HP Color LaserJet MFP M477fdn). Use the recommended drivers.</text>
      <sha1>19qreqarcaoly2eymx28xi568lqlar3</sha1>
    </revision>
  </page>
  <page>
    <title>Wiki tips</title>
    <ns>0</ns>
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      <id>292</id>
      <timestamp>2016-06-13T17:09:43Z</timestamp>
      <contributor>
        <username>Dstrodtman</username>
        <id>5</id>
      </contributor>
      <comment>Created page with &quot;Editing MediaWiki pages is easy. All users with an account should have privileges to add and edit pages.   ==Adding Pages== When adding a new page, it is a good idea to first...&quot;</comment>
      <model>wikitext</model>
      <format>text/x-wiki</format>
      <text xml:space="preserve" bytes="461">Editing MediaWiki pages is easy. All users with an account should have privileges to add and edit pages. 

==Adding Pages==
When adding a new page, it is a good idea to first create the organizational structure in the sidebar. Access the sidebar by navigating [http://depts.washington.edu/bdelab/wiki/index.php?title=MediaWiki:Sidebar here] and clicking the edit tab. Follow the format:
 * marks a section heading
 ** creates a page link (page_title|Page Title)</text>
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      <timestamp>2015-11-18T23:49:20Z</timestamp>
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        <username>Pdonnelly</username>
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        <id>2</id>
      </contributor>
      <comment>Pdonnelly uploaded a new version of [[File:FeedbackStar.jpg]]</comment>
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        <id>2</id>
      </contributor>
      <comment>Pdonnelly uploaded a new version of [[File:Gabors2.jpg]]</comment>
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** Data_Analysis|Data Analysis
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** Brain_Development_&amp;_Education_Lab|Home
** Data_Analysis|Data Analysis
** recentchanges-url|recentchanges
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** Data_Analysis|Data Analysis
** recentchanges-url|recentchanges
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
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* Acquiring and Analyzing Data
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
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* Data Analysis
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** Brain_Development_&amp;_Education_Lab|Home
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* Data Analysis
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** Data_Analysis|Data Analysis
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
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**Diffusion_Pipeline|Diffusion
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**MEG
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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**Diffusion_Pipeline|Diffusion
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
* Data Acquisition
** MRI_Data_Acquisition|MRI
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** ILABS_Brain_Seminar| ILABS Brain Seminar
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* TOOLBOX
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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** Cortical_Thickness|Cortical_Thickness
* Data Acquisition
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** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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** MEG_Pipeline|MEG
** Cortical Thickness|Cortical_Thickness
* Data Acquisition
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* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
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* TOOLBOX
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** Brain_Development_&amp;_Education_Lab|Home
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** helppage|help
* Data Analysis
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** Cortical_Thickness|Cortical Thickness
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* Lab Admin
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** ILABS_Brain_Seminar| ILABS Brain Seminar
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* TOOLBOX
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
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* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
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* TOOLBOX
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
* Data Acquisition
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* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
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* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
* Data Acquisition
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** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
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** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
* Data Acquisition
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** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
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** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
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** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
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** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
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** Cortical_Thickness|Cortical Thickness
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** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
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** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends_Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends_Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** Accessing Data
** Processing Data
** Data Organization
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** Accessing Data|HCP_Access
** Processing Data|HCP_Process
** Data Organization|HCP_Organized
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** HCP_Access|Accessing Data
** HCP_Process|Processing Data
** HCP_Organized|Data Organization
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** HCP_Access|Accessing Data
** HCP_Process|Processing Data
** HCP_Organization|Data Organization
* Lab Admin
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** HCP_Access|Accessing Data
** HCP_Process|Processing Data
** HCP_Organization|Data Organization
* Lab Admin
** System_Setup|Computer Setup
** wiki_tips|Editing This Wiki
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** HCP_Access|Accessing Data
** HCP_Process|Processing Data
** HCP_Organization|Data Organization
* Lab Admin
** System_Setup|Computer Setup
** NFS
** wiki_tips|Editing This Wiki
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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* navigation
** Brain_Development_&amp;_Education_Lab|Home
** recentchanges-url|recentchanges
** helppage|help
* Data Analysis
** Software_Setup|Software Setup
** Anatomy_Pipeline|Anatomy
** Diffusion_Pipeline|Diffusion
** MEG_Pipeline|MEG
** Cortical_Thickness|Cortical Thickness
** fMRI|fMRI
* Data Acquisition
** Data_Organization|Data Organization
** MRI_Data_Acquisition|MRI
** fMRI_Data_Acquisition|fMRI
** MEG_Data_Acquisition|MEG
** Behavioral|Behavioral
**Psychophysics|Psychophysics
* Human Connectome Project
** HCP_Access|Accessing Data
** HCP_Process|Processing Data
** HCP_Organization|Data Organization
* Lab Admin
** System_Setup|Computer Setup
** NFS|NFS
** wiki_tips|Editing This Wiki
** IRB|IRB
** ILABS_Brain_Seminar| ILABS Brain Seminar
* Useful Resources
** Reading_Instruction|Reading instruction/intervention
** Friends &amp; Affiliates|Friends &amp; Affiliates
* SEARCH
* TOOLBOX
* LANGUAGES</text>
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